2.1. Theoretical foundations

In the case of smartphones, a CBEC transaction’s success depends on the strategies put in place by e-sellers to convert subjective perceptions of buyers on the product (e.g. perception of quality, price, benefit, etc.) into buyer purchase intention and decision-making. The distance between buyer and seller requires e-sellers to develop strategies to convert buyers’ subjective perceptions of products into purchase intentions and decisions. Also important is the alignment of the buying intention and decision-making with the perception of the budget for purchases [29, 35].

Therefore, it’s critical to comprehend how a customer’s purchase budget, as well as the subjective perceptions of the buyer on the product, such as the quality, price, benefits, etc., influence his purchase intention. In this regard, [29] claimed that using the theory of planned behavior, customer perceptions of their purchase budget can influence consumers’ perception of the product. These perceptions, in turn, may impact the customer’s buying intent [21, 29, 38, 39]. In context, it has been suggested that TPB can be used to investigate the links between customers’ perception of purchase budget and customer purchase intention, as well as the mediating role of buyers’ perceptions [38]. From this theory [38] concluded that customers’ perception of purchase budget by influencing buyers’ subjective perceptions of the product, i.e., quality, price, and benefit.., could lead to influence their purchase intention. Based on that theory lens, we argue that when customers find a match between their perception of the purchase budget and their subjective perceptions of the product they wish to purchase, this will reinforce their purchase intention, which is a result of their positive perceptions of the product. Our argument seems to be supported by [35]. They pointed out that customers’ perceptions of the purchase budget could impact their perception of obtaining the values sought of the product they would like to buy, which in turn will reinforce their purchase intention.

2.2. Budget and purchase intention

PIT is defined as a type of behavioral intention that describes how likely a buyer is to make a future purchase of a certain product or service [40, 41]. In this perspective, the prior setting of a budget allocated to purchasing a service or product can be considered as the manifestation of the buyer’s intention to purchase and choose. Identifying factors underlying customer purchase intention has been featured as one of the primary objectives of sellers and brands in maximizing business benefits and leading to business success [8]. In the smartphone market, brands and sellers aim to attract and hold customers by concentrating on the elements that are known to influence purchase decisions. In order to successfully satisfy buyers’ desires, smartphone brands and sellers constantly want to be informed of the most significant and desired trends among consumers [42]. In this context, the relationship between customer purchasing capacity, as highlighted by his purchasing budget, and the desired product, should be a fruitful field in the smartphone-selling industry and for sellers striving to improve their customer purchase intention knowledge [7, 8]. The customer’s perception of the purchase budget is crucial in determining the customer’s choice perceptions, categorizing customer expenses and motivating its decision-making and purchase intention [8, 29]. Therefore, the customer’s perception of the purchase budget should be an essential foundation in the marketing and management field [29, 43]. Firms have been the first to recognize the importance of budget setting in management [43]. Since then, the budget has become a research subject in many industries [17], including the smartphone online selling industry [8]. In this optic, [7] suggested that customers’ perception of purchase budget concept should be included in future studies about smartphone purchases online to verify its effect on buyer purchase intention. Therefore, this current study somehow responds to [7, 8].

Researchers argue that the budget could significantly impact customer purchase intention [7, 8]. First, the customer’s perception of the purchase budget enables the customer to evaluate the affordability of products and identify different product brand alternatives based on the positive perceptions that the product generates [7, 29, 43]. Second, that positive evaluation of products enabled by the customer’s perception of the purchase budget strengthens purchase intention. In this context, most customers believe that they are more likely to base their purchases on allocated budgets for effective and efficient decision-making, which might have a favorable budget influence on purchase intention [8, 17]. [8, 29] noted that customer perception of the purchase budget serves as a decision-making incentive and protects and controls buyer reaction to purchase proposals while examining the benefits of such proposals. Therefore, the perception of the purchase budget before the transaction might positively affect the purchase intention and customer confidence.

Buying a product depends not only on the customer’s willingness to pay but also on his purchasing capability. When customers perceive a perfect suitability ratio between their purchase budget and service or product offers, that may increase their purchase intention. Especially in the online smartphone purchase framework. [7, 8] have noted that customer perception of the purchase budget might significantly impact customer purchase intention. According to previous literature, only a very limited number of studies have explored the linkage between customer perception of the purchase budget and purchase intention [7, 8, 29]. Additionally, previous literature suggests that the appropriate theory to explain that relationship is the theory of planned behavior (TPB) [29]. This theoretical framework specifies that planned behavior is determined by intention. From that perspective, one could conclude that the customer’s purchase intention in terms of quality or quantity, for instance, may be driven by the perception of his purchase budget, considered as the customer’s perception of his planned purchase action before the transaction. Thus, when a particular product or service matches the customer’s perception of his purchase budget, that may positively increase his purchase intention. With this theory, much research in marketing has revealed positive and significant relationships between different variables [44]. For instance, [29] reported a significant connection between mental budgeting attitude and purchase intention. Similarly, [45] reported a positive correlation between perceived behavioural control and eating intention. Therefore, the findings of previous studies confirm our belief that the impact of customer perception of the purchase budget on smartphone purchase intention through international online platforms may have a positive relationship. Hence, we postulate the following hypotheses:

H1. Customer perception of the purchase budget positively affects customers’ smartphone purchase intention through international online platforms.

2.3. Mediating effect in the budget-purchase intention link

As shown above, although topics related to the budget have triggered several bodies of research, there is still a limited understanding of various indirect and direct pathways that connect BGT to PIT in the online smartphone selling industry. The literature reviewed by [8] concluded that consumers might be sensitive to smartphone prices due to their purchasing capability. Accordingly, the possible customers first evaluate if they can pay the price of the desired smartphone before engaging in the transaction. In doing so, a customer’s perception of the purchase budget could determine their purchase intention. [7, 8] suggested, therefore, verifying the impact of customer perception of his purchase budget on his purchase intention and determining which way customer perception of his purchase budget could affect his purchase intention. In other words, what factors could mediate between customer perception of the purchase budget and purchase intention in smartphone purchases? However, since that suggestion, almost no studies, to our best knowledge, have investigated that proposal in the smartphone industry. Thus, almost four years later, [7] ’s proposal has not yet been investigated. The variables we have considered in this research include perceived quality, perceived price, perceived benefit, and purchase intention. As shown by [7, 16–18] it is evident that the different links (direct and mediating links) studied in this research are still not addressed in previous smartphone-selling literature. A summary of key studies that have examined the budget concept across various fields and industries is displayed in Table 1. As listed in Table 1, although the budget concept has been studied in some industries, it is clear that regarding the smartphone industry, not only has the budget concept not gotten much attention from scholars, but also the BGT-PIT link as well as the mediators suggested in this research have never been investigated. Therefore, this study considerably advances the earlier literature.

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Table 1. Budget concept study within different domains and industries.

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The link between customer perception the purchase budget and perceived smartphone quality in online sales has been neglected in the literature [7, 8]. A product’s PPQ is described as the consumer perception of the value or merit of a product [46]. In the case of smartphones sold online, [7] defines the perceived quality as the apprehension of a potential buyer about the excellent condition of the smartphones put up for sale. In this perspective, [8] pointed out that the customer perception of the purchase budget is a quantitative and qualitative expression perceived of a purchase plan and an aid in evaluating and motivating decision-making. That means that customers with a clear perception of their purchase budget may better perceive the product quality they choose. In the CBEC, due to the distance between buyers and sellers and the fact that customers cannot touch or feel products, their perception of product quality might be more based on their budgets [47]. The budgets, in turn, shape their choices [48]. In this regard, [32] shows that implementing purchase budgets reduces the possibility of a buyer waiting for better purchase opportunities. Customers who believe their spending plan is in line with the proposed offer in terms of price and quality can easily identify themselves with that offer [26]. That being so, the significance of customer perception of the purchase budget in the offline or online purchase setting is fueled by the perception of the customer’s demands and needs in terms of price and perceived quality [7]. Prior research indicates that the perception of the purchase budget can lead to a better reaction to purchase proposals by analyzing the future and present cost and quality benefits of such proposals [26, 29], thereby leading to improved perceived quality. In the CBEC framework, for instance, buyers come from different economic settings; therefore, considering the customer perception of the purchase budget can result in better sales. Therefore, smartphone companies need to offer products in terms of quality that consider potential buyers’ budgets [26]. Perceived price refers to a product’s actual price perception level compared with the customer’s reference price [49]. According to [50], the perceived price is one of the product’s fundamental values and positively influences customer purchase intentions. For that reason, buyers refer to that factor in computing their purchase budget. Implementing an appropriate purchase budget and considering the perceived price generally contributes to making a good choice [50]. Because that positively affects the evaluation of the different offers [34]. Therefore, consumers tend to be more satisfied when they see that the perception they have on a purchase budget they have planned to use to purchase a given product or service matches the perceived price [7, 8, 18].

Numerous studies have used the TPB as a foundation for comprehending the PB concept. As stated by the TPB, a person’s intention to adopt a specific behaviour derives from social environment factors and the evaluation of the perceived benefits [51]. PB describes buyers’ perception of what they will gain from a purchase [52]. Several studies have found that consumers’ perceptions that online purchases (including CBEC transactions) are beneficial are dictated by factors such as: getting more information about products, great flexibility, less effort in time, and competitive price [53, 54]. A customer with a purchase budget before purchase may easily take advantage of those factors. Therefore, his perception of the purchase budget stimulates him to track his spending and directly influences him while taking advantage of competitive prices [8, 29, 50]. The influence of customer perception of the purchase budget on customer purchase behaviour is also associated with positive customer perceptions of reducing spending [8, 7, 28]. Despite these pieces of evidence that customer perception of purchase budget might influence consumer perceived benefit when purchasing online, we found that almost no studies have explored that relationship, especially in smartphone purchases through CBEC. Hence, we formulate the following hypothesis

H2. Customer perception of purchase budget have a positive impact on (a) perceive quality, (b) perceived price, and (c) perceived benefit

Much research exists on the product-perceived quality-purchase intention link [46, 55–57]. However, the literature presents varied findings on that relationship. Some previous studies reported a positive relationship between these two variables. When studying psychological results on PPQ on PIT, [19] argued that there could be a positive relationship between PPQ and PIT [56], discovered a positive impact of PPQ on the PIT of certified products. In the same direction, [55] and [57] also highlighted a positive influence of PPQ on PIT. Similarly, utilizing TPB, [58] reported a positive link between products’ PPQ and PIT. [46] explain that perceived product quality is an essential signal that ascertains buyers’ purchase intentions. Therefore, it asserted that the fact that a customer attaches to a product brand over a competitor product is decided by its perceived quality level. That is also evident in the smartphone industry, where research showed a significant positive influence of PPQ on PIT [46, 58]. However, some studies in the smartphone and scooter or motorcycle industry reported an insignificant effect of PPQ on PIT, e.g., [7, 59, 60].

No doubt, perceived price is also represented in customer purchase intention. [19, 61] consider that perceived price acts as a mental guide that assists the customer in differentiating between products’ actual price and reference price on the markets. That allows the customer to decide whether the price is reasonable or not [58]. Indeed, when customers see the product price as reasonable, they perceive more benefits in buying that product, and that increases their purchase intention [62]. Therefore, customer perceptions of price act as drivers of purchase decisions [19]. For that reason, research in marketing has been dedicated to investigating the influence of perceived price on consumers’ purchase intentions [19]. Prior findings indicate that a high price tag may be a barrier to purchase probability [20]. In line with that, [19] also found that a high perception of price negatively influenced customer purchase intention. [21] highlighted a positive connection between PPR and PIT. In the smartphone industry, PPR is also a powerful indicator of PIT [7].

In the e-shopping environment, perceived benefit is viewed as the perception of the advantages gained from a shopping action [63]. When customers feel they are profiting from a transaction, it improves their commitment to purchase action and amplifies the certainty associated with the purchase [64, 65], explains that perceived benefits reinforce consumers’ trust in how much they would gain from an online purchase. Little research has focused on the perceived benefits-purchase intention link. However, the few who investigated that link highlighted a positive influence of PB on decision-making and purchase [51, 65] significantly. In this regard, [63] argued that customer purchase intention rises the more perceived benefits they receive. This has been observed in the online purchase of smartphones in the present environment of heightened competition [7]. Accordingly, it is hypothesized that PPQ, PPR, and PB may also positively affect customer purchase intention. Hence, we formulate the following hypothesis:

H3. (a) perceived quality, (b) perceived price, and (c) perceived benefit have positive effects on smartphone purchase intention through CBEC.

The literature mentioned above shows that considering the customer’s perception of the purchase budget can influence the customer’s perception of terms of quality (PPQ), price (PPR), and benefit (PB). Moreover, it has been shown that these customer perceptions could be crucial precursors to customers’ PIT.

Additionally, it has been highlighted that the customer’s perception of purchase budget (BGT) incorporates both the perception of his planning obligations and choice expectations [9, 10] i.e., his expectations in terms of price, quality, values, and benefit. Etc. TPB describes these perceived expectations as one of the foundations of customer intention [9, 10]. Moreover, TPB stipulates that customer intention is the closer determinant of customer behaviour [66]. In other words, a customer’s perception of his purchase budget (BGT) could determine his expectations perception, which could influence his intention and behaviour. Therefore, a customer’s perception of the purchase budget can assist in attaining positive perceptions of product quality, price, and benefits that, in turn, influence customer purchase intention. Due to the possible effects of customer’s perception of purchase budget on PPQ, PPR, and PB (H2), along with the effect of PPQ, PPR, and PB on customers’ PIT (H3), customer’s perception of purchase budget is predicted to have favorable indirect impacts on PIT through perceived quality, perceived price, and perceived benefit. Therefore, the study proposes that consumers’ PB, PPR, and PPQ mediate the effect of the customer’s purchasing budget on PIT. Hence, we formulate the following hypothesis:

H4. The connection between customer purchase budget and customer purchase intention is mediated by (a) perceived quality, (b) perceived price, and (c) perceived benefit.

2.4. Impacts of perceived price on perceived quality and perceived benefit

The marketing literature shows that the perception of a consumer on price can influence the customer’s quality perceptions and purchase probability [67]. Marketers support that idea because buyers are more inclined to think that more expensive and higher-quality goods must be priced higher [68]. Several studies have suggested that price could significantly affect the customer’s perceived quality [19, 68–70]. Nevertheless, a limited number of researchers have studied the connection between PPR and PPQ, although literature suggests a possible positive and direct relationship between these two constructs [71].

Marketers strongly believe that perceived quality is heavily dependent on customer perception of price [70], which is an essential factor in perceived benefits in online shopping [63]. The research highlighted that the buyers are prepared to buy more expensive services or products if the perceived benefit can compensate for the cost incurred [71]. In this direction, [63] consider the perceived benefit as an attribute deriving from price and essential to stimulate customer purchase intention online. [72] in the same direction, showing that perceived benefit is significant in online customer shopping behaviours and affects their decision-making. Accordingly, it is hypothesized that:

H5. Perceived price has a positive influence on customer perceived quality.

H6. Customer perceived price has a positive impact on perceived benefit.

2.5. The link between budget and PPQ, PPR, and PB across countries

The budget concept is extensively studied in the business industry, with a large number of research concentrating on offline sales settings. However, studies on purchase budgets investigating buyer perceptions through a cross-national optic in CBEC are scarce. Far further as we are aware, almost no study contrasts the impact of consumer’s perception of purchase budget on smartphone purchase intention made through CBEC markets in three different nations. As a result, cross-national research on samples from multiple nations is required to provide a more thorough understanding of the connection between consumers’ perceptions of purchase budgets and buying intention. Customers from diverse economic settings could present significantly different attitudes towards the consumer’s perception of purchase budget -purchase intention link. Most research has explored smartphone purchases in national and offline settings [7]. However, in the absence of certain types of smartphones on national and offline markets, potential buyers may only rely on CBEC markets.

We chose the USA, France, and Kenya for this study because:

1) These countries are significant countries in smartphone penetration rates in North America, Europe, and Africa, respectively [73–75] and 2) these countries are considered to be among the major countries in purchasing smartphones through CBEC in North America, Europe, and Africa [76, 77]. However, Kenya contrasts sharply with the USA and France regarding CBEC development levels. Nevertheless, although both USA’s and France’s CBEC industries are developed compared to Kenya, the USA’s CBEC industry is more advanced than France’s. [78, 79] indicate that B2C CBEC transactions only generated about $1000 billion in the USA in 2018, while they generated about $120 billion in France. These levels demonstrate the striking differences between the three nations’ CBEC industries. Figs 1 and 2 show the conceptual model and the significant countries by smartphone penetration rate. Fig 2 has been built using data from [73–75]. We can see from Fig 2 the smartphone penetration rate in countries considered in this study and other various nations. We can observe that the countries considered in this study (USA, France, and Kenya) are significant in smartphone penetration rates in their regions, i.e. North America, Europe, and Africa, respectively. Therefore, when comparing buyers from these nations, the conclusions of our study may not be the same. Hence, we hypothesize that:

H7. The impact of consumer perception of purchase budget on (a) perceived quality (b) perceived price (c) and perceived benefit (d) varies significantly by country (Kenya, France, and the USA).

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Fig 1. Conceptual model.

https://doi.org/10.1371/journal.pone.0279575.g001

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Fig 2. Smartphone penetration rate per country.

https://doi.org/10.1371/journal.pone.0279575.g002

3.1. Survey development, data integrity and respondents’ consent

We applied an SEM technique to test the study’s hypotheses using SmartPLS-4. PLS-SEM modelling is a well-known method for analyzing complex business research models [80]. A descriptive, exploratory, and empirical research design was used in this study. This study aims to identify the effect of customer perception of purchase budget on purchase intention through PPQ, PPR, and PB as mediators. Thus, a convenience sampling technique was chosen to carry out the study rather than a probability sample method [81]. A well-structured questionnaire was used to collect primary data for the study. The sample was collected from consumers who had recently purchased one or more smartphones through an international online shopping platform. The survey ran for about two months, from June 23rd 2022, to August 26th, 2022 through an online questionnaire distribution. We used two months to get more representative and accurate data. This duration includes data collection, and data cleaning. We created an online survey through Google Forms to collect the study sample (https://forms.gle/pS4GHXACnjNSe55i6).

Before the study began, the study received ethical approval from the committee in charge of research ethics and integrity at Harbin Institute of Technology, Shenzhen. In this direction, the research goal and survey were explained electronically to respondents. Minor customers (under 18) were not allowed to fill out the survey. All respondents gave their consent electronically before they were directed to the survey link and questions. In this regard, it was clearly written in the header of the survey that the survey was intended for academic research on smartphone purchases from CBEC platforms. Therefore, the information provided will be used for academic research purposes, including the possible publication of the results in an academic journal. The survey could be stopped by respondents at any time before it was submitted, preventing the storage of their responses. Only after respondents submit the survey at the end are their answers saved. A senior professor, head of the research data bureau in the faculty of Economics and Management at the Harbin Institute of Technology in Shenzhen, approved and validated the survey’s English version first. Then, the survey’s French version was translated by one of the authors of this study since his native language is French and further validated by a French language expert. We then shared the link with several online respondents (CBEC buyer groups) in France, Kenya, and the United States. The method of distributing online questionnaires was chosen because the framework of the study concerns the international online shopping market. Through this virtual framework, CBEB customers interact with e-sellers and each other, discussing and sharing their shopping experiences [82]. Therefore, the response rate would be higher as these frameworks are convenient for contacting CBEC buyers as they are the study’s target respondents [82]. The respondents came from three different countries: France, Kenya, and the USA. These countries are selected for their significant commitment to CBEC sales and smartphone purchasing through CBEC platforms [78, 79].

3.2. Demographic questions

Demographic questions, such as gender and age, as well as the CBEC platform names where the purchase was made, and smartphone brands purchased, were asked in the first section of the survey. However, due to the missing data on the CBEC platform names and smartphone brands purchased, we did not consider them in the model.

Data collected showed that the respondents’ minimum age was 20, above the minor age considered in the three countries analyzed. For instance, the minimum age in Kenya sample was 20, while the maximum was 48. In the samples of France and the USA, the maximum age was 43 and 45, and the minimum was 20, respectively. In France, 149 responses were ultimately deemed valid. According to the sample’s demographics, 78 respondents (52.34%) were men, and 71 respondents (47.66%) were women in France. The age was around 30. From Kenya groups, we collected 202 questionnaires. 62 responses were not considered because of missing data. Finally, 139 responses in total were deemed valid. Out of the 139 responders, the sample revealed that 66 (48.47%) were men and 73 (52.52%) were women. The average age was about 29 in the USA. 37 responses were not considered because of missing data. Finally, 142 questionnaires out of 178 in total were deemed to be valid. According to the USA sample, out of 142 respondents, 77 (53.91%) were women, and 65 (46.09%) were men. The US and Kenyan groups received the survey’s English version. However, we sent the French survey version to the French groups.

3.3. Survey questions

The second section had questions to gauge how consumers perceived purchase budget, product quality, price, benefit, and purchase intention. A 5-point Likert scale was used to evaluate the items, with 1 denoting strong disagreement and 5 denoting strong agreement. Based on the definitions of [7, 28, 48, 83], eight items were used to gauge how much the purchase budget was perceived. The scale used to represent the perception of the purchase budget was two-dimensional. Based on the research of [84], four items were used to measure perceived quality. Four items that were based on the work of [85] were used to measure perceived price. The perceived benefit was measured using five items designed based on the study of [54]. Five items that were modified from [7] to assess purchase intention were used. The items in each variable are listed in Table 2. The recommended sample size for PLS-SEM is approximately 10 times the total number of paths leading towards variables [80]. The conceptual model has 9 arrows in the current study; however, the sample size is 429, which is larger than what is required. As a result, the condition of representativeness is met. As was already noted, the research sample is far larger than what is required in each country.

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Table 2. Scales items.

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4.1. Validity of data and analysis

The descriptive analysis’s results (Table 3) showed that survey participants from Kenya had the lowest perceptions of each construct in the conceptual model considered in this study. Next came the French respondents. Regarding the United States respondents, they had the highest scores of the three nations.

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Table 3. Descriptive analysis.

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4.2. Common method bias (CMB), validity and reliability

A thorough study of collinearity was made to assess CMB using collinearity statistics (VIF). The test revealed that all collinearity inner values of latent variables are noticeably below the 3.3 cutoff point (Table 3). That indicates that the CMB is not an issue in this current research. The variables’ reliability was assessed using Cronbach’s Alpha and Composite Reliability (CR). The entire sample was first evaluated, and items with factor loadings of less than 0.600 were removed. In Table 4, the results of validity and reliability are shown together with the factor loadings of the remnant items for the sample of each nation and the entire sample. Alpha values and CR exceeded the advisable value of 0.700. The CRs supported convergent validity and Average Variance Extracted (AVE), which were all greater than or near 0.7 and 0.5, respectively. Cross-loading analysis was used to evaluate discriminant validity. The evaluation of the multicollinearity showed that the value of each indicator’s Variance Inflation Factor (VIF) was less than 5. Additionally, the Heterotrait-Monotrait Method (HTMT) and the criteria proposed by Fornell and Larcker were utilized to assess the discriminant validity of the data. Table 5 lists the outcomes of both analyses. Table 6 summarizes the cross-factor loadings for each item. A characteristic of discriminant validity is the fact that each of the factor loadings was greater than their cross-loadings.

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Table 4. Factor loadings, reliability and validity.

https://doi.org/10.1371/journal.pone.0279575.t004

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Table 5. Discriminant validity using Heterotrait-Monotrait (HTMT) and Fornell & Larcker (LK).

https://doi.org/10.1371/journal.pone.0279575.t005

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Table 6. Discriminant validity- cross loading.

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4.3. Structural model

We then evaluated the proposed hypothesized links in our conceptual model. Direct relationships were assessed first. Table 7 presents the outcome of that assessment. Table 7 presents the results for the entire sample as well as the findings for each nation. The results shown in Table 7 demonstrate that all other hypotheses were either negatively significant or insignificant, with the exception of H4: PPR-> PPQ (b = 0.408, t = 10.109, p = 0.000) and H5: PPR-> PB (b = 0.476, t = 10.273, p = 0.000). As a result, whereas H1, H2a, H2b, H2c, H3a, H3b, and H3c are rejected, only hypotheses H5 and H6 are accepted. Also, results show that samples from each country are largely consistent with those for the entire sample. For instance, in the Kenya, France and United states samples, the findings show that most hypotheses were likewise negatively significant, with the exception of H4:PPR -> PPQ (Kenya(b = 0.385, t = 4.328, p = 0.000); France ((b = 0.413, t = 5.951, p = 0.000)); United States ((b = 0.431, t = 7.276, p = 0.000)) and H5:PPR -> PB (Kenya(b = 0.479, t = 7.017, p = 0.000); France ((b = 0.376, t = 4.505, p = 0.000)); United States ((b = 0.569, t = 8.003, p = 0.000)). Therefore, with each country’s sample, hypotheses H1, H2a, H2b, H2c, H3a, H3b, and H3c are rejected, while hypotheses H5 and H6 are accepted.

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Table 7. Direct links between variables and mediation analysis.

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4.4. The analysis of the mediators

The results of the mediation links for each country and the total sample are shown in Table 7 (below). PPR (b = 0.395, t = 8.789, p>0.001) and PPQ (b = 0.011, t = 2.478, p>0.05) showed significantly positive mediating roles, according to the results for the entire sample, although PB (= 0.001, t = 0.584, p = 0.559) did not. As a result, hypotheses H4a and H4b are confirmed, and hypothesis H4c is refuted. Additionally, PB showed an insignificant mediating role for the samples of Kenya ((b = -0.001, t = 0.239, p = 0.811); France (b = -0.002, t = 0.368, p = 0.713) and the United States (b = 0.005, t = 1.029, p = 0.303). Therefore, hypothesis H4c failed for each group-specific (country). Concerning the mediating role of PPR, each nation results are in line with the results for the entire sample. For example, with the samples of Kenya, France and the United States, PPR (Kenya (= 0.347, t = 4.393, p> 0.001)), France (b = 0.437, t = 6.976, p>0.001) and the United States (= 0.437, t = 6.976, p>0.001)) showed significant and positive mediating roles. However, the mediating role of PPQ was insignificant according to the samples of Kenya (b = 0.006, t = 1.394, p = 0.163), France (b = 0.012, t = 1.208, p = 0.227), and the United States (b = 0.002, t = 0.176, p = 0.86), whereas it was significantly positive according to the overall sample, as already stated above. Therefore, hypotheses H4a and H4c are rejected, while hypothesis H4b is accepted in each country-specific sample.

4.5. Multi-group analysis

In the study’s last phase, we evaluated the variations in BGT’s effects in the nations covered in this research, i.e., Kenya, France, and the United States. The multi-group analysis was used to evaluate the H7 hypothesis (Table 8). The result showed that the variations were not especially apparent when contrasting BGT’s impact between countries. Therefore, our data did not support hypothesis H8a. These outcomes demonstrate that customer perception of the purchase budgets across Kenya, France, and the United States are quite similar.

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Table 8. Multi-group comparison (MGA) (H7).

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5.1. Managerial and practical implications

This study’s results will inform smartphone brand managers and CBEC e-sellers of the significance of links between customer perceptions of their purchase budget and purchase intention. Therefore, this research has substantial practical implications for e-sellers and smartphone brands that sell their products on international online platforms. First, it will guide them in implementing their sales strategies to attract more customer attention by offering products in line with the perceptions of the values customers wish to find in the product and their perceptions of the purchase budget. That would necessarily improve the customers’ purchase intentions. Second, e-sellers and smartphone brands thus must focus on smartphone offers that consider the standard of living. That would strengthen the links between the perception of the purchase budget and the perceptions of the critical values customers i. e., price, quality and benefits customers wish to find in the product. Doing so will strengthen customer purchase intention and improve the product’s positive image in the customer’s mind [87]. As [26] stated, buyers are generally more attracted to products when they perceive that the product could match their purchasing power. Their purchasing power supports their perception of the purchase budget. The perceived purchase budget appears as an essential factor which may be positively associated with the perceptions of the values that the customer is looking for in the product [27, 29] and positively associated with the customer’s purchase intention, as demonstrated in this current study. Smartphone industries should focus on product offers that strengthen the links between the perception of values that customers wish to find in the product they purchase and the perception of purchase budget. That would allow e-sellers to offer customers products matching customer purchase budgets.

This research has identified that PPR and PPQ significantly mediate between BGT and PIT in smartphones purchased on CBEC platforms. Consequently, the smartphone industry must give more importance to PPR and PPQ in their product design strategies by offering products that match customer perception in terms of price, quality and purchase budget to succeed easily in that market. Scholars have already pointed out that it is essential to implement that kind of strategy to ensure success within the CBEC marketplace [7, 78]. For instance, [88] concluded that smartphone brands should focus on more sophisticated quality attributes in the smartphone design they propose to the market since, because of social media, customer requirements in terms of quality will increase in future. In this direction, [27] showed that more than 90% of new products are unsuccessful in marketplaces due to quality and pricing attributes that do not match customer expectations and perceptions. Therefore, managers and smartphone manufacturers must establish a bridge between the pricing and quality expectations as well as perception, and the perception of the purchase budget, which will lead to a positive purchase intention.

5.2. Implications for theory

The study’s conclusions based on the total effect demonstrate that consumers consider the idea of the BGT since it was discovered to have a substantial impact of the BGT on PIT (without the presence of any mediator) in each country-specific sample and overall. It is also the case when considering the mediator PPR in each country-specific sample and overall. This suggests that consumer perception homogeneity toward BGT is responsible for aligning customer attitudes across different geographic locations. However, a noteworthy finding that varies among entire sample results concerns the insignificant mediating role of PPQ in the link between BGT and PIT in the samples from Kenya, France, and the United States but is significant in the overall sample. PPQ mediator’s overall impact on the relationship of BGT and PIT is the sum of the minor influences of PPQ through each subsample. There is a variation between PPQ mediator impact in the relationship of BGT and PIT from the overall sample to each country’s sample. When all of the responses are combined, the impact of the PPQ mediator becomes significant due to this aggregation effect. In this approach, smartphone e-sellers on international online platforms may manage customers more wisely. That will improve the likelihood of customers’ purchases. Smartphone e-sellers may use customer purchase budgets as a strategic tool to manage their connections with customers since they can easily propose customers’ products in line with their budget. This research established a perspective to understand the smartphone’s buying budget affects buyer purchase intention in smartphone selling through international online platforms. This approach will also help smartphone sellers, and brands better understand consumer behaviour and how they perceive purchase budgets (BGT) to develop effective marketing strategies.

5.3. Study’s limitations, future research and conclusion

This study has some limitations. First, only three nations were examined in this study (Kenya, France, and the United States). Future research should include other nations with different economic backgrounds to further study the influence of BGT on customer purchase intention. Second, cross-sectional survey data was used to assess the hypothesis that underpinned the study. Future research may concentrate on longitudinal data showing how BGT, PPQ, PPR, PB, and PIT are changing dynamically. Third, future research might determine the mediating role of other factors, such as e-seller trust or credibility, in the relationship between BGT and PIT. Additionally, despite the fact that PPR impacted PB, our conceptual model revealed that PB was an insignificant mediator. Therefore, PB should be included as a possible mediator in future research to continue researching its involvement in comparable conceptual models and attempt to come to definitive conclusions regarding the mediating role of PB in the relationship between BGT and PIT. Fourthly, no moderating factors’ impact was examined in this study. Future research might evaluate the moderating impact of smartphone brand popularity in each nation or brand availability in the local market.