Keywords

Innovative Smartphones, Intrinsic Cues, Extrinsic Cues, Social Trust, Perceived Value, Adoption Intention, Young Consumers, PLS-SEM, eWOM.

Introduction

Over the ten years or so since the inception of the first smartphones, the market for them has evolved greatly, with technology advancements driving innovation and digitalization placing more and more smartphones into consumers' hands. Today's smartphones don't just support communication; they're evolved into complex artificial intelligence (AI) fuelled, augmented reality (AR) powered, LiDAR–endowed, foldable, biometrically recognized, cloud-integrated, and advanced camera-equipped digital environments all by themselves (Yu et al., 2017). These technological developments have accelerated rivalry among smartphone producers, leading companies to strive for ever more unique products that can appeal to the more technologically advanced population.

Young consumers are among one of the most influential groups in the smartphone market because of their ability to be technologically aware, active in online communities and having high digital literacy in comparison to other consumer segments (Durand, 2018). Young consumers use a combination of social media platforms, online reviews, influencers’ recommendations and online word-of-mouth (eWOM) to make purchase decisions, making them more engaged with social media than traditional ones are(Yu et al., 2017; Dias et al.,2022). Accordingly, their ‘adoptability' is being increasingly shaped by their intrinsic design, technical specifications and performance quality as well as the external information cues such as peer recommendations and social validation.

Most research on technology acceptance relied on the previously established Technology Acceptance Model (TAM) by Fred Davis and the Unified Theory of Acceptance and Use of Technology (UTAUT) by Viswanath Venkatesh. In the context of technology adoption, these models highlight the perceived usefulness, perceived ease of use and behavioral intention (Dias et al., 2022; Lim et al., 2018).While these frameworks provide valuable perspectives into technology acceptance behavior, they do not offer much understanding of how social trust and other informational cues affect consumer decisions in digitisation contexts.

Cue Utilization Theory offers an important theoretical base to assist in understanding consumer evaluation behavior in uncertainty situations. (Rungruangjit, 2020) suggest that customers rely on intrinsic as well as extrinsic cues in judging product quality and deciding to buy the product. Intrinsic cues are on-product attributes like hardware characteristics, display features, design appearance, and technical capabilities; extrinsic cues are external informational cues like others' product reviews on the Internet and eWOM(Rungruangjit, 2020). What is presented in a technology-driven product like a smartphone is often technical and not easily assessable by the target consumer; in such a product, consumers rely strongly on socially communicated information to help decipher engineers' jargon and ensure that they can lower their perceived risk and uncertainty (Wisetsri, 2021).

Other than cue utilization, perceived value and social trust have been identified as major determinants of the adoption of the technologies. Perceived value is consumers' total judgments of the benefits received in connection with a product's costs (Wisetsri, 2021). Previous research has indicated that functional value, emotional value and social value are important factors that consumers identify in order to embrace innovative technologies (Yang et al., 2016; Fatima et al., 2019).Again, Social Trust is important in consumers' online buying behavior as it tends to help reduce potential uncertainty with respect to new products, that consumers depend on trusted information sources to help reduce (Fatima et al., 2019).

As digital communities and social networking sites become more influential in how and when to adopt technology, the need for trust in technology adoption decisions has become more pronounced. Before purchasing new smartphones, young consumers often look to their fellow users, user communities, online reviews and influencers for approval. This suggests that social trust could be an important remaining factor linking external indicators to the adoption intention.

Although there have been increased scholarly focus on the adoption behaviors associated with smart phones, little research studies the relationships between intrinsic cues, extrinsic cues, perceived value, social trust, and smartphone adoption intention in an integrated setting. Previous research has mainly focused on the technical aspects of technology and has comparatively less studied how social validation and the development of trust are utilized in product environments where consumers are digitally connected. Moreover, the effect of social trust on young consumers' intention to adopt smartphones is established through a limited number of studies.

The present research addresses this need by studying the role of intrinsic and extrinsic cue in influencing the adoption intention of the young consumers towards innovative smartphones, and perceived value and social trust serve as mediating variables in the process. With 450 respondents, the study tests the conceptual framework with the method of Partial Least Squares Structural Equation Modeling (PLS-SEM). Combining Cue Utilization Theory, Theory of Consumption Values (Yang et al., 2016; Byun et al., 2017).and technology acceptance frameworks that rely on TRUST, this study offers a stronger theoretical underpinning for the growing body of research on consumer technology uptake and has practical implications for smartphone manufacturers and digital marketers looking for ways to boost their adoption intention through social trust and digital engagement approaches.

Research Objectives

The study aims to achieve the following objectives:

• To identify the innovative technologies in smartphones from 2015–2025.

• To evaluate the awareness and usage of mobile technologies among young consumers.

• To examine the influence of intrinsic cues on young consumers’ perceived value toward innovative smartphones.

• To analyze the impact of extrinsic cues on perceived value and social trust.

• To determine the mediating role of social trust in the relationship between technology cues and adoption intention.

• To evaluate the effect of perceived value on young consumers’ intention to adopt innovative smartphone technologies.

Literature Review and Hypotheses Development

Cue Utilization Theory and Smartphone Adoption

According to the Cue Utilization Theory, consumers judge products based on cues provided on a Product, especially given uncertain product conditions and low levels of product knowledge (Han, Meng & Zhang, 2026). The theory classify the cues into intrinsic and extrinsic cues. The intrinsic cues are those attributes that are directly connected with physical or functional aspects of the product, while the extrinsic cues are external informational cues that are connected with the product environment (Binst, Michiels& Smets, 2026).

The significance of Cue Utilization Theory has grown greatly in current digital markets since customers are fed a vast quantity of information online prior to purchasing. Over the last several years, smartphone technologies have grown ever more advanced and integrated, from full AI support to foldable displays, augmented reality (AR), biometric authentication systems and cloud-based ecosystem connectivity. Innovation, however, can be technically complicated and leaves consumers with an uncertainty about product quality, reliability and long-term performance (Meena & Sarabhai, 2026).

Traditional methods of choosing products or services are increasingly being replaced by socially created product information that young consumers use to assess innovative smartphone technologies like influencer recommendations, YouTube reviews, online ratings and electronic word of mouth (eWOM) (Wang, Li &Yan,2026). In such environments, where peers share information about the products, a peer's product information might be more convincing than the firm-generated advertising (Nichols, Confente & Russo, 2026) because the peer information is more authentic and trustworthy than the firm's information.

Recent research also reveals yet another trend towards social validation processes driving digital consumer behaviour. For instance, many people see ticket counts, the number of reviews and influencer recommendations as proof of superior quality and credibility of the product (Gerling, Teubner & Braesemann, 2026). It's a move away from products-based evaluation, and a shift towards socially validated decision-making processes, especially in younger consumers who are using social platforms like Instagram, TikTok, YouTube and Reddit heavily.

While Cue Utilization Theory classically argues that both intrinsic and extrinsic cues are part of consumer evaluations, the recent global research about smartphone adoption suggests that, in the context of technology-intensive markets with limited differences in product technical specifications between competing smartphone brands, extrinsic cues might be more influential than intrinsic ones(Wu et al., 2026). When it comes to innovative smartphones, the user could perhaps more rely on community validation and social trust than technical evaluation.

Building on this work, the current study offers a refinement of Cue Utilization Theory by positing that the influence of intrinsic and extrinsic cues on the adoption of 'smartphones' is twofold: through different psychological processes. Specifically, the perceived value is mainly affected by intrinsic cues, while the social trust is mainly affected by the extrinsic cues and is indirectly affecting the intention to adopt.

Intrinsic Cues and Perceived Value

Intrinsic cues are cues innate or inherent to the product in question and those that consumers associate with the product's quality and performance (Lyu, Tang &Li, 2026).In the smartphone sector, intrinsic features encompass processor power, screen resolution and viewing capabilities, camera capabilities, battery life, operating system functionality, and design charm and durability, as well as cutting-edge technological advancements like AI-powered features and foldable displays.

Perceived value is the evaluative people make about the usefulness and yield of the product based on the effort that they put in to obtain it and use it (Abaah et al., 2026).Theory of Consumption values suggests that consumers consider many value dimensions when assessing a product, such as functional value, emotional value, social value and epistemic value (Gerling,Teubner & Braesemann,2026). New technology for smartphones can improve consumer perceptions of convenience, efficiency, entertainment and social stature, which leads to greater perceived value.

A number of recent research studies have been able to demonstrate that intrinsic product attributes have a positive relationship with perceived value and consumer purchase intentions in technology markets. For instance, have found that sophisticated smart phone attributes that contribute to perceived product quality significantly enhances consumers' emotional bonding with products. At the same time, found that certain innovative features of smartphones like AI technology for capturing photos and display technologies for creating immersive experiences have positive impacts on perceptions of functional and experiential value.

But, paradoxical results have been also reported in most recent studies. It has been suggested that consumer behavior with regards to the intrinsic cues is slowly diminishing because of innovation and creating a commodity effect in smartphones' markets (Aggarwal & Joshi, 2026). As many flagships smartphones have similar technical features, intrinsic features might start to be seen as just ‘regular requirements' and not really a differentiator for the smart phone buyer. But in today's world, consumers are more likely to rely on third-party users for the authenticity of innovation.

Although markets change over time, intrinsic cues are still relevant as they are the starting point of the product evaluation process of consumers. Even with that, technical superiority, innovative design and advanced functionalities can positively influence the consumers' value perceptions (if there is good social feedback).

Accordingly, the present study proposes the following hypothesis:

H1: Intrinsic cues positively influence perceived value toward innovative smartphones.

Intrinsic Cues and Social Trust

Social trust is the consumer's belief in the reliability, credibility and authenticity of information they get through social interaction or digital communities (Gao et al., 2026). Trust is an important factor in an online technology market due to uncertainty around innovative products, which stems from the information that is transmitted socially from one consumer to another.

The intrinsic cues, as to product quality and functionality, can be thought to influence social trust but their direct influence is not guaranteed based on theory. Technological features of a product like hardware perfection, processing speed and aesthetics may indicate technological advancements, but they cannot make the users trust in the technology itself, without reference to the external social sources (Gao et al., 2026).

Recent research has found that information that comes from peers and engagement in a community plays a bigger role than product specifications in forming trust among young consumers (Liem, 2026). When brands make claims about products on social media, they rely on the trust their social media users place in online reviews, social media influencers, and tech communities to feel confident about those claims. Technical specifications in smartphone markets are often viewed as marketing information, a thing that has to be complemented if it is to be accepted as accurate by relying on real-life user experiences.

Nevertheless, a few studies have corroborated that the quality of a product and the technology that it exploits can influence the trust-building process indirectly by indicating the trustworthiness and expertise of the brand (Dias et al., 2026). Modern and technologically advanced smartphones can be associated with greater credibility and less performance risk to consumers. However, such a relationship seems to be less pronounced than the influence of socially-generated information.

Current results have been inconclusive, leaving room for a limited role for intrinsic cues in socially interconnected consumption contexts. The variety of results in the literature suggests that intrinsic cues may have a limited effect in this socially interconnected consumer realm. Hence, the present study

H2: Intrinsic cues positively influence social trust toward innovative smartphones.

Extrinsic Cues and Perceived Value

Extrinsic cues are external information cues that are used to evaluate products in the decision-making process to reduce informational uncertainty (Dias et al., 2026). Extrinsic cues that are often present in a digital environment include online ratings, electronic word of mouth (eWOM), online reviews, brand reputation, peer recommendations, and endorsements from influencers.

Social commerce and digital communities have played an important role in making the explicit cues more significant in the context of technology adoption behavior. Social commerce and digital communities have contributed to making the explicit cues more important in technology adoption behavior. Online discussion forums, technology review websites and social networks are key influential factors that young consumers are turning to to assess the creativity of a smartphone technology (Fang & Li, 2026). It will help to fortify the idea that consumers have about the quality, usefulness, and emotional pleasure of a product or product attribute.

Empirical studies have recently shown that eWOM is an effective factor in augmenting perceived value in online purchase setting. (Gao et al., 2026) shows that consumers' perception of the product quality and that their expectation of buying the product grows after they read positive reviews online. Following this trend, (Sharma et al.,2023) discovered positive impact of influencer recommendation and peer-generated smartphone reviews on the generation-Z consumers' perceived value.

A few researchers, however, suggest that an overemphasis on promotional information or on sponsored influencers may lead to decreased trustworthiness for the various messages communicated and, therefore, a diminishing effect on value perception (Gao et al., 2026).Consumers are more aware of when their favorite influencers are being "influenced" (commercialized in the name of advertising) and some believe some online recommendations aren't true. So that can lead to being effective depending on perceived credibility and transparency from the extraneous cue.

These issues do not deter these concerns, however, as extrinsic cues are also important cuers, offering consumers experiential and socially confirmed information which they might not have been able to find out from product specifications alone. As a result, positive online interactions, and authentic peer recommendations are expected to improve consumers' perception of smartphone value.

H3: Extrinsic cues positively influence perceived value toward innovative smartphones.

Extrinsic Cues and Social Trust

Extrinsic cues are pivotal in forming social trust within digital consumers' settings. In online marketplaces, consumers often rely on external social cues, including eWOM (Electronic Word of Mouth), peer recommendations and influencers' recommendations, user ratings, and user-generated reviews, as sources of product credibility knowledge and to help overcome uncertainty(Lyu, Tang &Li, 2026; Fang & Li,2026).These informational signals are especially relevant in an area of the market where consumers do not have the technical knowledge to decide about the quality of the product on their own, such as a technology market.

Social trust is established when trusters believe that the information they received is reliable and accurate, and is believed to be from a trusted source (Carlsson, 2026). In the smartphone realm, kids first turn to digital communities, YouTube reviewers, social media influencers, Reddit discussions and technology forums before deciding on their buy. Positive online experiences and environment fosters consumers to be more confident in the performance of product, and helps remove the fear of purchase.

Extrinsic factors have been shown to have a great impact on trust building in the internet consumers' behavior in the latest studies. The quality of online reviews and their source's credibility negatively influences consumers' trust in buying a smartphone, according to (Carlsson, 2026). Likewise, (Hallikainen et al., 2026) noted that the technology reviews from peers and the authenticity of influencences significantly increase consumer trust and behavioral intention among Generation Z consumers.

As the influencer ecosystems and social commerce platforms come into the picture, the significance of social trust in digital decision-making has also increased. When measuring community engagement, some metrics like likes, shares, comments, and number of reviews are increasingly used to reflect credibility and popularity of products (Wisetsri et al., 2021). Thus, for the purposes of studying the use of extrinsic cues, they have a dual role as informational cues and as cues of social validation.

Recent literature, however, shows mixed trends that overcommercialisation of influencer marketing could lower consumer trust if they feel that the suggestion is inauthentic or sows a sense of obligation to buy( Fatima et al., 2019)Thus, an external information source's credibility and transparency then becomes critical in determining the effectiveness of external cues.

Nevertheless, most of the recent empirical studies have supported this view that good external indicators greatly influence social acceptance in technology adoption setting.

Accordingly, the present study proposes the following hypothesis:

H4: Extrinsic cues positively influence social trust toward innovative smartphones.

Perceived Value and Adoption Intention

Perceived value is well known to play an important role in factors driving consumer purchase and technology adoption intention. It is a reflection of the extent to which consumers realize the gains made on the product overall, compared to the costs of getting it and using it (Yang et al., 2016) Innovative smartphones mean perceived value takes on three forms: Functional benefits of smartphones are technological efficiency and convenience; Emotional benefits of smartphone include enjoyment and excitement; Social benefit of smartphone includes prestige and peer approval.

According to the theory of consumption values, consumers compare products with respect to a number of value dimensions, such as functional value, emotional value, social value and epistemic value (Ramos, de Almeida & Fernandes, 2019). Advanced features like Artificial Intelligence-enhanced photography, foldable screens, next generation gaming features, AR integration and connectivity with various eco systems can have a significant impact on the value perception of consumers on user experience and lifestyle convenience.

A number of recent studies have shown that perceived value are positively related to adoption intention and technology acceptance behavior of SM.A number of recent studies have evidenced positive relationship between perceived value and technology acceptance behavior (TAB) and adoption intention (AI). For consumers, the functional and the emotional values are statistically significant determinants of their willingness to use innovative technologies for their smart phones, according to (Park & Kim ,2019; Jones, 2014) found that perceived value not only positively affects the purchasing intention but it also diminishes the uncertainty of the consumers about technological difficulty and value justification of the price.

However, others researchers suggest that the perceived value is not the only factor that can determine the adoption behavior in socially connected digital environment. However, even if products are seen as useful, consumers are reluctant to use them when they don't trust online information sources or if there's no peer validation (Jones, 2014; Balakrishnan & Dwivedi, 2021) This means that social trust can play a role with perceived value, but not exclusively that it is different from perceived value in influencing behavioral intention.

Even though these debates, previous studies suggest that perceived value has a positive impact on consumers' adoption behavior. Thus, it can be expected that consumers who see innovative smartphones as more functional, emotional and social advantageous will have higher intention to adopt them.

H5: Perceived value positively influences adoption intention toward innovative smartphones.

Social Trust and Adoption Intention

In an environment reliant on technology, one of the most influential factors that influenced consumer behaviour and intention to use technology online is social trust. Trust is a consumer's sense of reliability, credibility and authenticity in the social production of information received via the internet, social networks, and peers (Hsiao, Lin& Wu,2022).

For consumer markets with a high level of investment in technology, such as mobile phones, consumers may be uncertain about technology functionality, compatibility, durability, and satisfaction after purchasing the product. Thus, young consumers are often relying on online reviews, influencers content, consumer-to-consumer discussions, and social networking sites to confirm information related to products and purchases (Byun et al., 2017). Thus, social trust can be seen as an important driver to decrease perceived risk and increase behavioral intention.

Empirical studies that have been undertaken in recent times consistently confirm that trust has a significant impact on intention to buy online as well as on technology adoption behavior. (Fatima et al., 2019) revealed that consumers' willingness to adopt innovativeness mobile technologies increases with trust placed in peer-generated smartphone reviews. Likewise, (Wisetsri et al., 2021) found that the trust placed in online communities and review sites greatly affects the intention to buy a smartphone among digitally active customers.

With the rise of social commerce — and platforms like Instagram, TikTok, YouTube and Reddit — the need for trust within digital consumption has become even more pervasive. Users tend to view these genuine user experiences, honest reviews, and community activity as signs of product reliability and credibility (Park & Kim, 2019). Fear of negative reviews and skepticism about online information on the other hand, can deter adoption behaviors despite e-products' sophisticated technological attributes.

Yet, too much exposure to conflicting information online can lead to information overload and skepticism among consumers, which could hamper trust-based decision-making processes (Yang et al., 2016).Though the worry, most of the recent research backs up the argument of a continuing strong relationship between social trust and the behavioral intention in online technology adoption contexts.

Accordingly, the present study proposes the following hypothesis:

H6: Social trust positively influences adoption intention toward innovative smartphones.

Mediating Role of Social Trust

Social trust serves as an important psychological mechanism by which individuals filter and interpret the external information cues and actively form intentions. Consumers are often in doubt about the quality, authenticity and technological reliability of a product in the digital purchase environment. The strategy of increasing the trust in the content source associated with the information increases in the case where this information is socially transmitted via an online review, a recommendation from peers or a post from influencers (Yu et al., 2017).

It is not necessary to directly look at the influence of extrinsic cues on smartphone adoption, but can indirectly be mediated through the social trust building process. Authentic peer experiences and credible online reviews can boost consumers' confidence in new smartphone innovations and spur adoption of the same (Dias et al., 2022).It means that trust is also an intermediate process between social generation information and consumers' behavioral intention.

Some recent studies back up the conceptualization of trust as a mediator of online consumer behavior. In technology markets, (Wisetsri et al., 2021) discovered that online review quality has a mediated relationship with purchase intention via trust. Constructions in the field of social commerce have a significant impact on consumer behavior.

The mechanisms by which intrinsic cues can affect adoption intention through social trust are not well understood, however. While technology and product quality could improve the value assessment, the consumers might first need to validate the value before they can trust on the innovative technologies of a smartphone. Especially among young ‘consumers', claims about a product often need to be validated and endorsed by an association of peers and community before being believed.

Based on these reasons, it is proposed that the present study will investigate the mediating role of social trust between the intrinsic and the extrinsic cues and the adoption intention for innovative smartphones.

Therefore, the present study proposes that social trust mediates the relationships between intrinsic cues, extrinsic cues, and adoption intention toward innovative smartphones.

H7a: Social trust mediates the relationship between intrinsic cues and adoption intention toward innovative smartphones.

H7b: Social trust mediates the relationship between extrinsic cues and adoption intention toward innovative smartphones.

Research Gap

Despite the many studies so far that have investigated smartphone adoption behaviour with commonly used technology acceptance models (TAM and UTAUT), certain interesting research gaps have not been adequately addressed. First, previous research has focused more on functional utility, ease of use, technological performance, and distracted more on how socially-generated information and trust-building processes influence smartphone adoption behaviour.

Secondly, research has typically focused on the opposite ends of the spectrum: intrinsic cues, extrinsic cues, perceived value, and trust, but not in a cohesive model. With the rise of social media environments, the presence of influencer culture and online communities, a digital consumer world is gaining greater influence from informational cues in terms of intention to adopt, thus leaving room for researching the influence of each individual informational cue as well as their interaction.

Third, modernity in recent lattice structures in the smartphone market is an important factor related to commodity technologization. Third, there is a commodity technologisation in the modern product market of the smartphone. As a result, consumers might increasingly use a process of external social validation when judging innovative technologies in the smartphone rather than based on the intrinsic qualities of the product. Yet little empirical support is available on how intrinsic vs. extrinsic cues may differ in their effects in this context.

Fourth, while trust plays a crucial role in online consumer behavior, few studies focused explicitly on the role of social trust between the technological cues and intention to adopt the smartphone among the young consumers.

Lastly, many of the existing research works have been carried out on general online purchasing behaviour instead of the adoption of novel innovations for smartphones, which are equipped with artificial intelligence integration, foldable display, augmented reality and ecosystem connectivity. Given that these are the identified gaps, the present study engages with these gaps by developing and testing an integrated model which consists of perceived value and social trust as mediators between the two different types of cues and adoption intention among young smartphone users.

Theoretical Foundation and Conceptual Integration

Based on the theory of Cue Utilization Theory, Theory of Consumption Values and perspectives on the trust placed in technology, the present study aims to explain the adoption intention of young consumers related to innovative smartphones. Learned integration of these theoretical points gives a full spectrum of outlook for the comprehension of the development of value evaluation for technologically advanced products in digitally connected surroundings.

According to Cue Utilization Theory (Cheung, 2022) the consumers will use informational cues to evaluate the quality of the product and reduce the uncertainty in the decision making process. According to the theory there are two types of cues, the intrinsic cue and the extrinsic cue. Natural cues are directly linked to product property function and aesthetic attributes such as design, technical performance, display characteristics, and innovative features. Extrinsic cues are those external sources of information comprising brand reputation, online reviews, influencers' recommendations, and emails word-of-mouth (eWOM). With highly innovative and technologically complex products like smartphones, consumers might have trouble making a rating system before buying, and more and more rely on socially created stories to give them guidance to make decisions (Park & Kim,2019).

Cue Utilization Theory accounts for consumer's processing of information related to the product information but Theory of Consumption Values by explains the mechanism of forming value perception of the consumers. The theory assumes that multiple dimensions of value impact consumer choices: functional value, emotional value, social value, epistemic value and conditional value. In the smartphone arena, the incorporation of such cutting-edge components as AI camera systems, foldable displays, augmented reality (AR) interactions, and the integration of the smartphone ecosystem can positively impact consumers' functional and emotional value perceptions. But, in the digital markets, the importance of social value based on endorsement from peers and online validation has grown.

The present study further draws these perspectives on trust in technology adoption to explore how consumers minimize uncertainty of the existence of innovative technology. It is recognized that trust is a key factor in online consumer behavior since information asymmetry and perceived risk are considered key features of a digital buying environment (Förster & Schulz, 2022).For many young users, online communities, influencers, peer reviews and social networking platforms serve as authoritative sources to verify the quality and reliability of a product before they adopt it (Stangl et al., 2023). As such, social trust could be a mental shortcut influencing the adoption intention. Hence, it is possible that the adoption intention is mediated by social trust as an extrinsic cue.

The interconnection of these theoretical frameworks is especially germane when considering Amazon smartphables. The adoption of the innovative smartphones is especially relevant to the interconnection of these theoretical frameworks. The existing technology adoption models, namely the Technology Acceptance Model (TAM) (Förster & Schulz, 2022) and the Unified Theory of Acceptance and Use of Technology (Lyu, Tang & Li,2026). Mainly focus on perceived usefulness as well as perceived ease of use as predictors of behavioral intention. Yet, such models lack explanation of the role of social transmission of information and trust building processes in digitally connected consumer contexts.

The present study strives to overcome this limitation by positing a model that extends the intrinsic product utility or functional attributes to include the cues of information perceived as socially validated to influence perceived value and trust among young consumers when deciding to adopt a smartphone. The study thus continues to build on Cue Utilization Theory by showing the different functions that intrinsic and extrinsic cues serve when being adopted by a technology. The value perceptions are mainly formed by the intrinsic cues while the level of social trust is mainly built by the extrinsic cues, which then impacts on the adoption intention.

In addition, the paper adds to the trust-based adoption literature by placing social trust as a critical mediation mechanism between informational cues (extraneous) and consumer behavioral intent. This theoretical convergence offers a more all-encompassing understanding of the adoption of technology in today's digital markets where the presence of influencer ecosystems, social commerce, online reviews, and community-driven decision-making process play a crucial role.

Conceptual Framework

The present study aims to develop a conceptual framework to understand the relationships of intrinsic cues, extrinsic cues, perceived value, social trust, and innovative smartphone adoption intention based on literature from the Cue Utilization Theory, Theory of Consumption Values, and trust based on technology adoption literature Figure 1.

In the framework, IIIR is considered as an independent variable, PV and ST are considered as a mediator, and AT is considered as a dependent variable.

Research Methodology

Research Design

This research is conducted using quantitative and explanatory research design to explore how the effect of intrinsic cues and extrinsic cues on the intention to adopt an innovative smart phone by young consumers. The method of data collection from the respondents, i.e. gathering of their data at one point in time was done by the cross sectional survey method. The Quantitative research design is used as it addresses the measurement of hypothesized relationships among multiple constructs that are latent in nature in a way that is empirical, using statistical modelling techniques (Abaah et al., 2026).

This study adopts a deductive approach that takes an outlook from Cue Utilization Theory, Theory of Consumption Values and trust-based technology adoption perspectives. The conceptual framework contends with several linked relationships and mediation effect(s), so a secondary method of analysis was chosen: Partial Least Squares Structural Equation Modeling (PLS-SEM).

PLS-SEM is well suited to exploratory and predictive research models with complex relationships between the latent constructs and mediation paths/processes (Gerling, Teubner & Braesemann, 2026). Moreover, PLS-SEM can be applied to research involving the behavioral intentions and technology adoption in new situations within digital environments.

Target Population and Sampling Technique

The study sample for the study included young smartphone users who can be described as young people who are active in the use of digital technologies and online information platforms. The reason for choosing "Young consumers" is that they are one of the most technologically connected demographic segments and “electronic word of mouth” (eWOM), “social media” (SM), and communication from “influencers” all heavily influence them (Gerling, Teubner& Braesemann, 2026).

To conduct the study, particularly since the target population could be reached through the convenience by digitally active smartphone users the study did not use random sampling.For the study there is no random sampling because the target population can be reached through the convenience of digitally active smartphone users in this context. In technology adoption and consumer behavior research of populations that are digitally connected, convenience sampling is extensively used, even if it comes with some compromises in generalizability over the power of probability sampling techniques (Gao et al ., 2026).

There were 450 valid responses collected and analyzed. An adequate sample size for PLS-SEM analysis. Higher sample sizes of over 200 are needed to drive statistical reliability and predictive accuracy of complex structural models with mediation effects, as stated by (Fang & Li, 2026). The number of samples that have been used here are therefore regarded as statistically sufficient for the present study.

Questionnaire Design and Scale Development

The development of a questionnaire and scale in the same manner.

A structured questionnaire was used for primary data collection, where the instruments used are questionnaires that have been validated from the parameters in the previous study. This was a two-part questionnaire that included: Demographic data about the respondents, Measure items associated with the study constructs

The following constructs were measured in this study:

• Intrinsic Cues (Int)

• Extrinsic Cues (Ext)

• Perceived Value (PV)

• Social Trust (ST)

• Adoption Intention (AI)

All measure items were adapted from existing studies, which warranted content validity and construct reliability. Items were slightly adapted to accommodate the context for innovative uses of the smartphone.

An item-by-item scoring of the questionnaire was done on a five-point Likert scale ranging from:

1 = Strongly Disagree

to

5 = Strongly Agree.

Consumer behaviour and technology adoption studies are widely using multi-item Likert scales to accurately assess the magnitude of psychological constructs that are not observed (Binst, Michiels & Smets, 2026).

Pilot Study and Content Validity

A pilot study was performed to judge the clarity, readability and content validity of the items in the questionnaire prior to actually conducting the final survey. 50 people, who represented the target population, were interviewed during the pilot survey.

The results from the pilot study were used to further aid in clarifying wording and improving item clarity of the questionnaire while eliminating item ambiguity. Some minor changes were made for readability and context relevance to the smartphone consumers.

In addition, the questionnaire was subjected to a review process to meet the face and content validity and face reliability of the academics in the field of consumer behaviour and technology adoption research. This pilot testing process facilitated preliminary level of reliability and aided in measuring the constructs as intended in the questionnaire.

Data Collection Procedure

Data was gathered using digital communications with social media distribution of the survey online. It was felt that on-line data collection would be appropriate as the target respondents were digitally active smartphone users who are used to online interaction and discussions related to technology.

The respondents were told the purpose of the academic research prior to their participation. There was a complete voluntary response and informants were told that responses and answers would be kept confidential and anonymous. During the survey process no personally identifiable information was collected.

Prior to statistical analysis the final dataset was checked for incomplete responses, missing values and inconsistencies in the degree of response.

The study adhered to the established ethical guidelines and standards of research involving human participants, while ensuring confidentiality.

Results and Analysis

Measurement Model Assessment

The measurement model demonstrated satisfactory reliability and validity across all constructs. Cronbach’s alpha values ranged from 0.824 to 0.905, while Composite Reliability values exceeded the recommended threshold of 0.70, confirming strong internal consistency reliability.

Similarly, convergent validity was established, as all Average Variance Extracted (AVE) values exceeded the recommended threshold of 0.50.Indicator loadings were statistically significant and predominantly above 0.70, indicating acceptable indicator reliability.

Discriminant validity was assessed using the Fornell-Larcker criterion and HTMT ratio. The square root of AVE for each construct exceeded inter-construct correlations, while HTMT values remained below the recommended threshold of 0.90 .These results confirmed that the constructs were empirically distinct and suitable for structural model assessment.

Hypotheses Testing

The bootstrapping analysis supported six out of eight proposed hypotheses.

Extrinsic cues significantly influenced:

• perceived value (β = 0.546, p < 0.001),

• and social trust (β = 0.808, p < 0.001).

Social trust significantly influenced adoption intention (β = 0.570, p < 0.001), emerging as the strongest direct predictor in the model. Perceived value also positively influenced adoption intention (β = 0.296, p = 0.047).

In contrast, intrinsic cues demonstrated only a marginal effect on perceived value (β = 0.282, p = 0.052) and no significant effect on social trust (β = −0.083, p = 0.535).

The mediation analysis further confirmed that social trust significantly mediated the relationship between extrinsic cues and adoption intention (β = 0.460, p < 0.001). However, the mediating role of social trust between intrinsic cues and adoption intention was not supported.

Overall, the findings indicate that young consumers rely more heavily on socially validated informational cues than on intrinsic technological attributes when evaluating innovative smartphones.

Internal Consistency Reliability

Internal consistency reliability measures the degree of consistency of the indicators that measure a construct as a latent variable. The Cronbach's alpha (α) and Composite Reliability (CR) were used for assessing reliability in this study. Values greater than 0.70 suggest that the instrument is reliable Table 1.

The results revealed that all constructs had a satisfactory reliability score > 0.70. Cronbach's alpha for all constructs was high, ranging from 0.824 to 0.905, and the Composite Reliability was >0.876 in all cases. This Study shows that the Social Trust indicators had the highest reliability values, which meant they had a high level of consistency between their indicators.

Indicator Reliability

The reliability of the indicators was evaluated by the outcomes of the confirmatory factor analysis performed with Smart PLS 4.0 on the outer loadings. According to (Hair et al. 2022), the loadings of the indicators should be greater than 0.70 or at least greater than 0.50 in exploratory studies.

Most of the loadings were higher than the reliable value of 0.70 showing high reliability of indictors. As the loading of ECP1 was just slightly under 0.70, it was still above the acceptable threshold of 0.50 and was significant. Hence, all of the indicators looked at Table 2.

Convergent Validity

Convergent validity is the degree of the variance common to the set of indicators that measure a construct. The convergent validity was tested with the Average Variance Extracted (AVE). AVE scores of >0.50 are considered acceptable for convergent validity Table 3.

AVE exceeded recommended value (0.50) which indicated good convergent validity. The construct with the highest AVE (0.725) showed that a significant portion of variance was explained by the indicators of the construct, social trust.

Discriminant Validity

Discriminant validity indicates the extent to which constructs are distinct across the various dimensions. Both Fornell-Lacker criterion and Heterotrait-Monotrait Ratio (HTMT) were used in the present study to assess discriminant validity Table 4.

The general levels of the HTMT were found below the recommended liberal threshold of 0.90 by (Henseler et al., 2015) reflecting acceptable discriminant validity among constructs. Constructs were empirically discernible with some approximate values, but overall, the results have validated the empirically difference between constructs and simultaneously demonstrated meaningful theoretical relationships Table 5.

Structural model assessment

After the reliability and validity have been set, the structural model was evaluated for the proposed relations between constructs. Collinearity analysis, coefficient of determination (R²) and effect size (f²) were conducted in the structural model assessment as well as the procedure of hypothesis testing through bootstrapping.

Collinearity assessment

Variance Inflation Factor (VIF) was used to check for collinearity among constructs of predictors. (Hair et al., 2022) suggested that if VIF values were less than 5, there are no problem of multicollinearity Table 6.

The model accounted for 67.1% of the variance in Adoption Intention, which is a significant portion of the variance. Likewise, a significant portion of the variance in Perceived Value as well as Social Trust could be accounted for by intrinsic and extrinsic cues.

Coefficient of determination (r²)

The model accounted for 67.1% of the variance in Adoption Intention, which is a significant portion of the variance. Likewise, a significant portion of the variance in Perceived Value as well as Social Trust could be accounted for by intrinsic and extrinsic cues Table 7.

Effect size (f²)

The individual contributions of each of the exogenous constructs to the endogenous constructs in the proposed structural model were determined by calculating the effect size (f²). Small effects are described by f² values of 0.02 or less. Medium effects, of 0.15 or higher; and large effects, of 0.35 or more Table 8.

The findings show that percieved value (f² = 0.410) and social trust (f² = 0.888) were influenced by extrinsic cues to a large extent. This discovery suggests that online reviews and peer recommendation, and eWOM have a strong influence on the value perceptions and the trust forming by consumers on innovative smartphones. Social Trust also showed a big effect on Adoption Intention (f² = 0.411), which places importance to it in affecting consumer behavior.

A small-to-medium effect on perceived value was found with intrinsic cues (f² = 0.109), while social trust received only a negligible effect from intrinsic cues (f² = 0.009). It implies that just products' technical specifications and product-related characteristics do not necessarily form the basis for trust with young consumers, without social validation effects.

Structural model assessment

Multicollinearity problems were not found using collinearity diagnostics and all Variance Inflation Factor (VIF) values are considerably less than 5.0.

The structural model had moderate fit between fit measures and the overall model. The model explained:

46.4 % of the variance in self-control, and Both these factors accounted for 67.1% of the variance in adoption intention.

The model had strong predictive ability with an R² value higher than most acceptable values for behavioral research.

The effect size analysis also identified that the extrinsic cues had a large effect on perceived value (f² = 0.410) and the social trust (f² = 0.888). In contrast, intrinsic cues had limited influence on quality perception, and no effect on social trust. Compared to the mediating variables, social trust significantly more strongly influenced the level of adoption intention than did perceived value.

Social trust and intention to adopt. The model shows the extrinsic cues have more power on affecting perceived value and social trust than the intrinsic cues. The coefficient of determination (R²) values suggest that a high degree of explanatory power is achieved for two dependent variables: adoption intention (R² = 0.671), perceived value (R² = 0.577) and social trust (R² = 0.572). The structural paths further give evidence that social trust is the strongest predictor of young consumers' intention of adoption.

Mediation Analysis

Figure 2 illustrates the structural relationships among intrinsic cues, extrinsic cues, perceived value, social trust, and adoption intention. The model demonstrates that extrinsic cues exert a stronger influence on both perceived value and social trust compared to intrinsic cues. The coefficient of determination (R²) values indicate substantial explanatory power for adoption intention (R² = 0.671), perceived value (R² = 0.577), and social trust (R² = 0.572). The structural paths further reveal that social trust is the strongest predictor of adoption intention among young consumers.

Discussion of Findings

The results of the study show that the source of the cues has a major effect on the adoption intention of young consumers of an innovative smartphone. Perceived value and social trust were greatly affected by extrinsic cues such as online reviews, eWOM, social media conversations, and peer recommendations. The results reveal that there is a strong link between extrinsic cues and social trust that indicates a strong dependence on socially transmitted information when considering innovative technologies.

These findings support the theory of Cue Utilization which states that the consumer relies upon the outside information cues to further clarify complex purchase situations. With the technical aspects of smartphone devices, such as hardware and software, becoming more standardized across the various companies and brands, consumers seem to be focusing on social validation over the technology.

The results also indicate that the most vital factor influencing adoption intention is social trust. The younger generation of consumers who are willing to trust online reviews and information coming from peers are more likely to embrace innovations in smartphones. This result is consistent with earlier research on trust in technology consumption, which has been so important in understanding digital purchase contexts as a phenomena.

Intrinsically cues had only a slight influence on perceived value while no significant effect was found for social trust. This means that the products features are not enough to convey consumers' confidence to purchase and use the product unless there is another social stimulus. New or cutting-edge smartphone features can be seen as "standard service" for young people rather than a factor establishing a difference.

This mediation analysis also protects the findings of the previous analysis presented above that social trust is one of the key psychological mediators that links to the extrinsic cues with the adoption intention. The main impact of consumer sentiment on the outcome of online reviews and peer recommendations is in reinforcing trust in socially-generated information. As such, the results emphasise the increasing significance of Digital Communities, Influencer Ecosystems and eWOM in the process of smartphone adoption.

Theoretical Implications

The literature to date on consumer behaviour, smartphone adoption and technology acceptance would benefit greatly from the present study which creates an integrated theoretical framework of Cue Utilization Theory, Theory of Consumption Values and trust based perspectives of technology adoption. Grounded in the prior research, the findings highlight that, besides the intrinsic characteristics of the product, the social trust is more powerful factor impacting young consumers' intention of purchasing innovation.

First, the study empirically confirms the differential effect of intrinsic and extrinsic cues in consumer environments in the digital domain, thus contributing to the Cue Utilization Theory. The results of the present study suggest that whilst there are both intrinsic and extrinsic cues affecting the evaluation of a product (Olson & Jacoby, 1972), the present research shows that young consumers' adoption behavior is strongly influenced by extrinsic cues. This indicates the consumer is more likely to rely on information transmitted among one another rather than directly from the product for products with technologically sophisticated features.

Second, the study adds to the literature on trust in technology adoption by establishing the relationship between social trust and intention to adopt as the strongest one. The well-known models that have been introduced at present, the Technology Acceptance Model (TAM) and the User Technology Acceptance (UTAUT), are mostly focused on functional utility and performance expectancy. The current study however provides solid evidence that trust in peer-generated information, online communities and eWOM has a stronger influence on intention to adopt than the perceived product functionality itself. The discovery points to the growing significance of social proof mechanisms for online purchasing.

Third, the study enhances the understanding of the Theory of Consumption Values where it clearly shows that informational influences form factors outside consumers can have a strong influence on their value perceptions. Nevertheless, perceived value still has some impact on intention to adopt, but not to such an extent as compared with social trust. This discovery implies that such perception of value might not be enough to influence the adoption behaviour even if there's the trust in the sources of information generated socially.

Lastly, the results offer valuable information about the adoption behavior of the youth in relation to smart phones. The earlier research about smartphone adoption was mainly centered in the technological usefulness, user-friendliness and innovation attributes of the Smartphone. In this respect, the present study brings a major contribution to the literature, by highlighting the importance of online reviews, social recommendations, online communication via influential individuals and peer validation in the adoption process. The results thus provide evidence for a shifting towards a socially constructed practice of using smartphones in digital ecosystems.

Finally, this model showed that a significant degree of the model's explanatory power (R² = 0.671 for adoption intention) indicated that the model is comprehensive in explaining the adoption behavior of using innovative smartphones in users. The study was thus theoretically sound and could explain consumer behaviour in the more dynamic and socially networked technology markets.

Managerial Implications

The results of the present study have a number of significant managerial implications that are useful to smartphone manufacturers, digital marketers, technology brands, and social commerce platforms as well as to the influencer ecosystem in order to boost intention to adopt among young consumers.

Execute Trust centred Marketing strategies.

The results show that the highest factor effecting the intention to use a smartphone is social trust. The results indicate that it is crucial for smartphone companies to shift their focus from the traditional feature-based advertising approach and focus on trust-based digital marketing methods.

Beyond just brand communication, the focus of consumers is more often on authentic peer experiences, online communities, and socially validated information. Thus, companies should be increasingly aware of creating clear, honest and trustworthy digital communication practices.

Smartphone brands should:

• expose fake reviews on social platforms and other locations,

• encourage customer reviews, and

• demonstrate actual experiences of users, and

Be transparent about what is involved in influencer partnerships and clearly highlight those partnerships.

Trust-building mechanisms will garner more significance than mere product superiority in engaging digitally connected consumers.

Ensure powerful influence and creation communities.

With its powerful effect on the behaviors associated with smartphone adoption, the influence of extrinsic cues is a growing concern.

Technology influencers come in various capacities and can be found on various platforms including:

• YouTube,

• Instagram,

• TikTok,

• Reddit,

• and X (Twitter)

Hangers are vital to youth consumers, providing them both confidence and certainty, and substantiation about product quality.

But the results indicate that there is a need for credibility too. Consumers could be skeptical of content that is very commercialized or scripted for a promotion. So, the smartphone manufacturers need to take care of the following:

• long-term influencer partnerships,

• authentic product demonstrations,

• unscripted reviews,

Engages in and uses community involvement strategies.

Audiences trust micro-influencers and technology reviewers more to the extent that they share their own experiences with the products they review, in turn leading to more productive adoption results than celebrity recommendations, which do not feel as genuine.

Companies need to be careful about who they choose to affiliate with for influencers because they need to match both size of audience and also:

• credibility,

• engagement quality,

• community trust,

• and perceived expertise.

Use Social Commerce & Community Based Marketing

The results suggest that social construction is growing stronger in determining decisions of smartphone adoption within Digital Ecosystems.

This implies need for prompt investment by companies on:

• online brand communities,

• social commerce platforms,

• peer discussion forums,

• and participatory digital ecosystems.

Platforms such as:

• Reddit communities,

• Discord groups,

• Telegram channels,

• YouTube review communities,

Their and Facebook technology groups.

can greatly influence the consumer's trust and intent to use.

Brands should encourage:

• user interaction,

• peer support,

• product discussions,

• experiential sharing,

• and collaborative engagement.

Community involvement approaches care effectively reinforce:

• social validation,

• brand loyalty,

• and consumer trust, both short- and long-term.

Implement AI Based Personalization and Recommender Systems

The results have significant implications for the AI-driven digital marketing systems.

Consumer appetite for the socially trusted information means that brands and companies dealing with smartphones and e-commerce should feature AI-powered recommendation systems that can provide the following elements:

• personalized product suggestions,

• trusted reviewer content,

• customized peer recommendations,

Also, there are and intelligent review filtering systems.

In addition to auto-hailing, AI sentiment analysis tools could also enable businesses to keep track of:

• consumer trust,

• online sentiment,

• Real time reputation risks - current and future.

In very competitive smartphone markets, AI-powered systems for managing trust can be strategic tools for building consumer trust and intent to use.

Digital Reputation Management.

Results show that social trust is a strong variable and critical when making the argument that negative online sentiment can have a significant impact on smartphone adoption intention.

For this reason, companies ought to put in place proactive digital reputation management tactics, including:

• routinely checking online shopping reviews,

• quickly adapting to adverse situations,

• providing training on how to handle complaints, and

To be active members of digital communities, and to participate in these communities actively.

Platforms such as:

• YouTube review channels,

• Reddit discussions,

• Twitter/X conversations,

Digital technology for content on TikTok.

Can quickly enhance positive as well as negative customer expectations.

Therefore, companies need comprehensive tools to monitor social media in real time and detect and respond to new trust concerns before they start to rise to a reputation threat.

Putting product innovation into practice by utilizing social validation. repositioning product innovation through social validation.

A takeout from the study is that, innovation can no longer ensure consumers will adopt the technology.

The findings suggest that consumers' demands for social validation have grown as intrinsic phone characteristics have also become a part of perceived value. Thus, firms should shift the narrative of technological innovation from technical aspects to socially acceptability ones.

Instead of promoting, for example:

• processor speed,

• AI capabilities,

Or perhaps just the megapixels of the camera,

brands should demonstrate:

• real-life user experiences,

• creator-generated content,

• social utility,

• and community endorsement.

To support and get more consumer engagement by offering what consumers want consumers will react more positively to:

How does the technology enhance Real experiences?

Rather than merely technical performance measures.

Gather support from the Early Adopters to become Trust Ambassadors.

The results also indicate that trusted instituzioni like early adopers and technology fanatics are likely to play a pivotal role in digital ecosystems in the future.

Firms should therefore:

• understand key opinion leaders, and

• deliver products into market quickly; and

• add a variety of records and notes, and

• and foster user advocacy programs.

When trusted early adopters have good experiences with the product, it can result in social validation, which can lead to higher rates of wider adoption.

Such a tactic could be especially pertinent with Generation Z consumers, who are the users whose decision-making is most heavily reliant on peer-driven digital communities when assessing new technologies.

Digital data may also have implications for eCommerce and platform managers.ECommerce and platform managers might be affected by digital data as well.

The results are also applicable to:

• e-commerce platforms,

• review systems,

• and Digital marketplace operators.

Platforms should enhance:

• review authenticity verification,

• fake review detection,

• trust indicators,

• and recommendation transparency.

If consumers are unsure that online information is accurate, their intent to use it could be limited. As a result, the trust established over platform infrastructures are key factors shaping digital purchase behaviors.

These are the ecommerce platforms that can continue to keep the following things:

• review credibility,

• transparent algorithms,

Interactions with real and genuine community.

Are expected to create consumer confidence and increased technology adoption.

Conclusion

The present study examined the role of intrinsic and extrinsic cues on young consumers' adoption intention of innovative smartphones while perceived value and social trust were included as mediators. The study was based on the Cue Utilization Theory, Theory of Consumption Values, and the perspectives of technology adoption based on trust, which led to the construction and empirical testing of an integrated conceptual framework by applying the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach.

The conclusions draw that the external factors are significantly greater than the internal properties of the smartphone in terms of influencing smartphone adoption behavior. Providing access to Internet-based reviews, recommendations from their peers, communications with influencers and giving Internet-based word of mouth by itself had a positive impact on both perceived value and social trust of young consumers. In comparison, no significant influences of intrinsic cues on social trust (or perceived value) were found.

Most importantly, social trust proved to be the most robust predictor for the intention for adoption and was also a statistically significant mediator between the effect of the extrinsic cues on adoption behavior. This study suggests that young consumers are increasingly turning to information from social agents and trust-based mechanisms from community groups to assess new technologies in their smartphones.

The study thus illustrates the fundamental shift in the practice of evaluating a product, in the case of smartphones, from a product centric to processes of decision-making using social validation. In digitally connected markets, where consumers are exposed to information via social channels and influencers, and social commerce and online communities are a feature, consumers seem to appear to have favoritism toward socially transmitted information over solely technical assessments.

Findings extend Cue Utilization Theory by distinguishing between the psychological roles played by intrinsic versus extrinsic cues, as they were found to have distinct roles. The main factors for the formation of the valuation are the intrinsic ones, although extrinsic ones have a relatively strong influence on the level of social trust which in turn affects the behavioral intention. The study also extends the trust-based technology adoption body of knowledge by incorporating social TRUST as a key predictor in the smartphone adoption process.

Further, the results do not fit the existing technology adoption models, which mostly regard the perceived usefulness and technological performance as predominant determinants of the intention to adopt. The findings imply that social validation, online credibility, and digital trust infrastructure are becoming more and more influential factors in shaping consumer behavior in today's technology markets.

Practically, the research shows that there is a strategic relevance of:

influencer ecosystems,

• community-driven marketing,

• social commerce platforms,

• online reputation management,

Integrating trust-building measures that are used in combination with artificial intelligence.

For the smartphone industry aiming to earn consumers' intentions, it is now more important than ever to skirt the path of mere product marketing and focus on creating genuine social connections and trust-driven communications.

The study offers a detailed explanation of the adoption behavior of smartphones in increasingly digitalized world where smart devices are integrated, and brings valuable insights to consumer behavior, digital marketing and technology adoption studies.

Limitation and Future Research

The present research has some limitations that can lead to further research.

First of all, the study was conducted in a cross sectional type of research design, therefore capturing consumers' perception at a single point in time. Adoption behavior of technology is in nature dynamic and may change over time when consumers become more familiar with new innovations on the smartphone. Longitudinal research designs should be adopted in future studies for investigating the effect of the intrinsic cue, the extrinsic cue, the perceived value and the social trust over various stages of the cue adoption process.

Secondly, the study concentrated on young consumers, who have a high level of activity in digital environments and social media. This is a very valuable target group to study smartphone adoption, and the findings here might not necessarily be applicable to older age groups, or individuals who aren't as technologically advanced. Future work could investigate generational buying habits, analyzing generational differences in reliance on social trust, as well as the trust placed in professionals and less digitally engaged consumers.

Thirdly, the study focused only on innovative technologies of the smartphone. The relative effect of intrinsic and extrinsic stimuli can vary among other emerging technology areas, including :wearable devices, smart home technologies, electric vehicles, metaverse technologies, fintech applications, and AI-based products for consumers.

The suggested framework could then be extended to other industries utilizing alternative technologies in the future.

Fourth, the study did not explore the moderating moderating variables that can be a possible factor influencing the adoption behavior of technology as a choice. Future research may inform the study of the moderating effects of :technological expertise, digital literacy, consumer innovativeness, brand loyalty, perceived risk, cultural orientation, Level of engagement on social media (intensity) and with websites.

These factors can help provide a greater understanding of differences in how consumers are dependent on socially created information.

Fifth, this study depended on the self-reported responses of the survey items and therefore could potentially be suffering from common method bias and social desirability effects even after the statistical procedure used, and described earlier, was implemented. Going forward, future studies could include: behavioral analytics, experimental designs, clickstream data, sentiment analysis, Gain insight to and social media interaction metrics

To increase causal inferences and limit self-report biases.

Sixth, due to the dynamic and evolving environment of digital ecosystems, it is likely that technology and smartphone adoption behavior in the future are likely to be further moulded by :AI-generated recommendations, Algorithmic trust systems, virtual influencers, generative AI reviews, Social interaction and in the metaverse.

However, future studies need to explore the impact of new AI-powered communication systems on consumer trust formation and the technology adoption process.

Lastly, future research could be conducted on cross-cultural differences of adoption behavior among mobile phone users. Social validation and trust in the community may have more influence on consumers in a collectivist culture than on consumers in an individualistic culture. Cross-country studies of technologies might then offer useful insights into how socially driven technology adoption takes place in different cultures.

The current study offers a basic format to support understanding of the changing nature of how trust in social and informational cues in the time of smartphone adoption behavior. As digital ecosystems evolve even more to influence ways consumers communicate, however, continued study will be vital to understanding how technology will evolve in socially connected markets.

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Received: 31-July-2026, Manuscript No. AMSJ-26-17357; Editor assigned: 03-Aug-2026, PreQC No. AMSJ-26-17357(PQ); Reviewed: 17-Aug-2026, QC No. AMSJ-26-17357; Revised: 24-Aug-2026, Manuscript No. AMSJ-26-17357(R); Published: 01-Sep-2026