Conspicuous Consumption in the Market for Smartphones Journal Article Peer Reviewed

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Open Access Article

A Study of Consumer Repurchase Behaviors of Smartphones Using Artificial Neural Network

Department of Industrial and Management, Kyonggi University, Suwon, Gyeonggi 443760, Korea

Received: half-dozen August 2020 / Revised: 17 August 2020 / Accepted: 18 August 2020 / Published: xix August 2020

Abstract

This study analyzed consumer intention to repurchase a smartphone, using an analysis of previous research and suggesting this every bit a research model. To this terminate, factors influencing "intention to repurchase" the smartphone were derived as social influence, consumer satisfaction, emotional loyalty, and addiction. In addition, statistical assay was conducted to investigate consumer repurchase intentions and the causal relationships. Information technology is likewise possible to investigate whether consumer habits are linked to repurchases by analyzing their psychological inclinations; therefore, in this study, 390 people who repurchased a smartphone over the past two years were surveyed, and data were collected. This study analyzed the causal relationships amongst the factors using SPSS 24.0. Moreover, the causal human relationship assay was enhanced using the artificial neural network (ANN) algorithm. With multiple regression analysis and the ANN algorithm, consumer satisfaction (0.71), emotional loyalty (0.108), and social influence (0.062) were determined to impact the intention to repurchase. This means that the ANN algorithm tin can be used over multiple regression analysis and improve the results of the analysis. In improver, this study also provided practitioners with a way to improve their understanding of consumer behavior intention to repurchase smartphones.

1. Introduction

1.1. The Business Phenomenon

Google Insight and the global market research firm CCS predicted that 1.six billion smartphones would be sold in 2016, with an increase to two billion sold by 2019. Southward Korea has the highest smartphone penetration rate at 92%, followed by Nippon (64%), Federal republic of germany (75%), the Us of America (United states of america) (78%), and the United Kingdom (United kingdom) (77%).

With the penetration rate of smartphones being so high in the Korean market, it is more constructive to motivate existing consumers to repurchase rather than to focus on new consumers and markets. According to Table 1 [1], smartphones (especially Samsung) have a very high marketplace share, only consumers prefer to repurchase Apple'due south iPhone over Samsung's Galaxy.

Companies ofttimes pay big amounts of money to increase customer loyalty and lure customers away from their competitors. Verizon, a leading United States (United states) telecommunications service provider, launched an unlimited programme equally an aggressive marketing strategy to secure customers from competitors [2]. Other providers in the same manufacture, including T-Mobile, AT&T, and Sprint, offer smartphone installment plans and early termination fees to customers who switch from competitors to their services instead. Beyond that, many smartphone manufacturers offer marketing promotions that greatly reduce the purchase price of smartphones when customers sign up for more than than two years of service through alliances with mobile communication service providers. In that style, each company actively implements marketing strategies in mature markets to protect their customers and attract those of competitors. After all, companies know that retaining existing customers is more profitable than finding new ones. Added to that, companies' repurchase strategies often revolve around event promotion. Although all of those strategies tin can temporarily increase sales, there is a limit to maintaining sustainability. Therefore, companies carry enquiry on the capacity of diverse marketing strategies to prompt repurchases among existing customers. With advanced technology, consumers can hands find the products and services they want; however, from a business concern standpoint, differentiation is becoming more than challenging. It is very difficult to create and provide products or services that are superior to competitors and that cannot be imitated in the bodily business organisation market. As a issue, many companies must compete fiercely within the aforementioned market place for consumers. In a saturated market, marketing costs often focus on retaining existing consumers rather than new ones; therefore, many companies recently adopted marketing strategies to secure their make loyalty. It is very of import to maintain consumer loyalty in order to increase profits in such a competitive market situation; inquiry shows that, if the service industry reduces its consumer bounce rate past five%, profits will increment by 25–85% [3]. In other words, from a marketing standpoint, research indicates that managing relationships with existing consumers is more efficient than attracting new consumers and that consumer loyalty is positively related to corporate profit [4,5].

Smartphones have a faster repurchase cycle than other electronic devices; consumer-friendly smart devices (like smartphones) have a lifecycle of only 2.77 years [6]. Because smartphones are used more ofttimes and consumed more rapidly than other electronic devices, it is very important to clarify the factors affecting repurchase intention of this production with a short repurchase cycle. Thus, this study analyzes factors affecting the repurchase of smartphones by South Korean consumers, using the bogus neural network algorithm.

1.2. Inquiry Questions

The enquiry began with the following questions: "Why practice consumers repurchase smartphones?" and "What factors touch consumer behaviors of smartphone repurchase?" Thus, this study examined the repurchase factors that influence consumers when they repurchase a smartphone, through the following questions:

• What are the factors that affect smartphone repurchase?

• How does consumer recognition of smartphone make relate to consumer satisfaction and purchasing habits (continuous intention to use)?

• What exercise the quality and ease of utilize, equally perceived by consumers, have to do with consumer satisfaction and purchasing habits?

ii. Theoretical Groundwork

2.ane. Theory of Reasoned Action (TRA)

Ane of the well-nigh of import areas of consumer psychology and behavior enquiry is the human relationship between consumer attitudes and behaviors. One theory explaining consumer attitudes and intentions to use a production is the theory of reasoned action (TRA). The TRA suggests that consumers carefully consider the consequences of various behaviors before acting [7]. In other words, consumer beliefs is under voluntary control; thus, consumer behaviors can be predicted via their intentions [7]. In add-on, the TRA considers subjective norms, in comparison with other models that explain consumer attitudes. Consumers consider the costs of performing their deportment and the benefits that may arise every bit a result of the action before choosing the activity that is the well-nigh benign/least plush [8,9].

2.2. Heuristics Theory

Heuristic thinking refers to intuitive thinking through experience, rather than analyzing conclusions based on rational thinking [ten]; in other words, information technology involves bias. A heuristic involves satisfaction with "bounded rationality" rather than pursuit of an impossible real rationality [11]. According to heuristics theory, many consumers make decisions based on habits, beliefs, or by post-obit others' decisions, as these approaches are simpler and avoid complications.

This study adopted the heuristics theory as a basis from which to study consumer habit.

ii.3. Artificial Neural Network (ANN)

An artificial neural network (ANN) can be divers as an array of highly connected bones processors called neurons. As shown in Figure 1, the multilayer perceptron (MLP) has the same hierarchical construction as a neural network, with at least one intermediate layer between the input layer and the output layer. The MLP has a construction similar to a single-layer perceptron, but it improves the network ability by nonlinearizing the input and output characteristics of the intermediate layer and of each unit, thus overcoming the various disadvantages of the single-layer perceptron. In other words, as the number of layers increases, the properties of the MLP are more than enhanced [12].

X₁, Ten_two, and X₃ accept weights W₁, W₂, and W_iii associated with these inputs. The output Y of the neuron is calculated every bit shown in Figure 2. The office f is nonlinear and is chosen the activation function. The purpose of the activation function is to innovate nonlinearity into the output of a neuron, which is important because near real-world data are nonlinear [12].

In mathematical terms, the neuron k depicted in Figure 3 tin can be described past the post-obit equations:

$$u_k={{\displaystyle \sum }}_{j=1}^m{w}_{kj}{x}_j,$$

(one)

where φ() is the activation role,

$u_{\mathrm{yard}}$

is the linear combiner output for the input signals,

$b_k$

is the bias, and

$y_k$

is the output signal of the neuron [12].

3. Literature Review

3.ane. Intention to Repurchase

Researchers give a great deal of attention to consumer intentions to repurchase. Henkel et al. (2006) concluded that satisfied consumers have increased service usage levels and increased intentions of future usage [13]; moreover, while examining the importance of satisfaction, Cronin, Brady, and Hult (2000) discovered that consumer satisfaction and repurchase intentions can exist increased by offering added value and quality services [xiv]. Repurchasing and the factors that influence it were investigated by many scholars [15,16,17,18,19,20]. Repurchasing behavior is defined as a consumer's actual beliefs of purchasing the same product or service on more than one occasion. The majority of consumer purchases are repeat purchases [21]. Consumers often repeatedly buy similar products from like sellers, and near purchases represent a series of events rather than a single isolated event. Retention is another mutual term for repurchase [22,23,24], which is considered to exist ane of the most of import variables in human relationship marketing [25]. Repurchase is the actual behavior of the consumer; however, intention to repurchase is defined as the consumer'southward determination to participate in future activities [26].

3.two. Factors Causeless to Affect the Intention to Repurchase

Information technology tin be inferred that keeping existing consumers is more important than attracting new consumers in a competitive surroundings such equally the smartphone market place. Most marketing inquiry focuses on consumer satisfaction and consumer intention to use products and services, with the assumption that consumer satisfaction indicates repurchase. However, it is essential to report a multifariousness of consumer behaviors in society to more comprehensively understand consumer repurchase behaviors; therefore, it is necessary to examine factors that deviate from consumer satisfaction-centered repurchasing research. Thus, this study analyzes other factors affecting intention to repurchase.

3.three. Consumer Satisfaction

Oliver (1997) institute that consumer satisfaction differs from the joy experienced when purchasing products or services [27], and their expectations were predictable to be better than their experiences. In addition, the study found that consumer satisfaction is reflected in the evaluation of one's emotions about a product or service. Rust, Zahorik, and Keiningham (1995) showed that consumer satisfaction (a consumer'southward willingness to revisit or recommend) has a strong impact on loyalty [28]. They too plant that consumer satisfaction is influenced by diverse aspects (product, service quality, shop attributes, and corporate marketing activities). Wen et al. (2011) suggested that satisfaction positively affects online repurchase intention [29].

3.4. Social Influence

Social influence is regarded every bit a disquisitional element in decision-making past people in sociology and in behavioral science. In this study, social influence refers to the extent to which people's social networks influence their behaviors [30]—i.e., the ways in which a person's beliefs, attitudes, thoughts, and actions modify equally a result of their social interactions [31]. This definition is rooted in social influence theory.

The TRA [7,nine] suggests that a person's behavioral intentions depend on their attitude toward the behavior, along with other subjective norms. A subjective norm is the influence had past the people in one'south social surround on one'southward behavioral intentions (i.e., the perception of whether people who are of import to them think that they should perform the behavior in question). The concept of subjective norms greatly influenced the formation of the measures of social influence in these two models, as well equally many other studies. Venkatesh and Davis (2000) believed that, in voluntary settings, social influences are more likely to operate indirectly through commonsensical outcomes [32].

Currently, discussion of oral fissure—a form of social influence—every bit a marketing communication strategy is famous and globally characterized as a price-constructive and persuasive promotional tool [33].

three.5. Emotional Loyalty

Law, Hui, and Zhao (2004) [18] used Oliver's definition of loyalty equally "a deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-make or same-brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior". In other words, they viewed loyalty as a consumer attitude rather than a consumer behavior. Behavioral loyalty is solely viewed as a repurchase of the product or service. Dixon et al. (2005) found that loyal consumers are expected to consistently repurchase despite competitive efforts [34]. Emotional loyalty is the ultimate blazon of loyalty, in which consumers choose a particular brand because they accept a personal connection with the brand, regardless of price, convenience, or other external factors. Attractive looks, novel materials, and atypical design technology bring positive emotions to consumers, thereby providing emotional value to them.

Arruda-Filho et al. (2010) recruited iPhone users for interviews and conducted a netnographic assay that showed consumers may experience social value due to the possession and apply of smartphones [35]. They found that consumers may retrieve of iPhone ownership as a symbol of luxury and high social status [35]. In add-on, sharing the experience of using smartphones also helps consumer interpersonal relationships. When consumers perceive higher social value from a production/brand, they bear witness greater brand loyalty behaviors, such as disseminating positive information and paying premium prices [36]. Pihlström and Brush (2008) revealed that, when consumers perceive greater emotional value in a product/brand, they show more than brand loyalty—every bit measured past repurchase intentions, willingness to pay, and positive word of mouth [37].

3.6. Addiction

Because repetition is a central characteristic of everyday life, habit research is of import for understanding consumer behaviors. Approximately 45% of people'southward behaviors are repeated almost daily, usually in the same context [38]. Chiu et al. (2012) and Limayem and Cheung (2007) found that consumers tend to purchase products habitually [39,twoscore].

Studies by Anderson and Sullivan (1993) and Jones and Sasser (1995) emphasized the need for all-encompassing research on repurchase intentions [v,41]. In particular, research on consumer psychology actively seeks to secure long-term competitive advantages through favorable relationships betwixt companies and consumers. In a number of previous studies, researchers suggested that, when consumer satisfaction (consumer empirical operation) increases with superior quality of products and services, consumers are more willing to return for purchase. Therefore, companies tin look to increase their sales and institute a audio consumer base past getting consumers to repurchase. Despite the importance of the intention to repurchase, nigh research focuses on consumer satisfaction, merely one factor of that intent.

4. Enquiry Hypotheses and Research Model

4.1. Research Hypotheses

Early studies of consumer behavior explored the relationship between repurchase and satisfaction; even so, this human relationship is non straightforward. Fornell (1992) studied positive correlations between consumer satisfaction and consumer retentiveness [42]. Wen et al. (2011) found that satisfaction had a positive consequence on online intention to repurchase [29]. Tsai, Huang, Jaw, and Chen (2006) discovered that satisfied consumers were more probable to continue their human relationship with a particular arrangement than dissatisfied consumers [43]. This view is supported by many researchers [19,22,41,44,45,46,47,48,49,50]. Withal, Mittal and Kamakura (2001) plant that the satisfaction–repurchase relationship could be disrupted due to iii principal reasons [19]. Additionally, Olson (2002) revealed that, despite the general view that satisfaction is associated with repurchase, few empirical studies associated satisfaction with bodily repurchase behavior [51].

Kamakura (2001) pointed out that establishing a directly link between satisfaction assessment and repurchase behavior is not easy for many organizations [19]. In addition, the satisfaction–repurchase human relationship can exist influenced past various characteristics of the consumers. Despite equal ratings given on satisfaction, repurchase beliefs differed significantly, which was attributed to differences in consumer age, instruction, marital status, sex, and residential area [nineteen]. Many factors complicate satisfaction–repurchase relationships. The trouble is that researchers practise not consistently define the relationship across studies, which tin can be operationalized as behavior, attitude, or complex [52].

Consumer satisfaction tin can occur during different stages of the shopping process (before, during, and after), during the purchase of dissimilar types of goods (convenience, shopping, and specialty) [53], and in a traditional or online setting. In addition, different types of consumers exist [54], and they all have varying levels of cognition almost the product [55], which affects their level of satisfaction.

Understanding the importance of a comprehensive review, the study attempts to summarize previously reported findings in order to explain the complex relationship between satisfaction and repurchase. Cognition of consumer satisfaction and their repurchase behavior will improve companies' ability to develop more effective marketing strategies in the future [56]. Previous studies demonstrated that overall consumer satisfaction with services is strongly related to behavioral intent to reuse the same service provider [57]. Therefore, in this study, the following research hypothesis is established:

Hypothesis ane(H1).

Consumer Satisfaction Positively Impacts Intention to Repurchase.

This work also studies the importance of social influence on repurchase intentions [58]. Social influence refers to actions, feelings, thoughts, attitudes, or behaviors related to private modify through interaction with other individuals or groups. In social psychology, it is often associated with the touch on of social norms on changes in personal behavior and attitudes [59]. Purchase decisions are related to the need to be respected, and social value is derived past acquiring desirable social status [60]. Some observations were made that consumers exercise not shop lone. Peers, families, and other groups strongly influence individual purchasing decisions. These reference groups practise word-of-oral fissure marketing and can play an active role in influencing the opinions of others. That influence is sometimes negative or positive in terms of the interests of certain organizations [61].

Hypothesis 2(H2).

Social Influence Positively Impacts Intention to Repurchase.

Emotional loyalty is behaviorally expressed by retention [59]. Furthermore, customer loyalty is well recognized as a significant driver of repurchase intention in the online marketing literature [62].

This emotional and affective connectedness influences consumer beliefs (retentiveness, brand repurchase, positive give-and-take of oral fissure) [63]. Brand loyalty is expressed as a tendency to continuously purchase the aforementioned make [64].

Repurchase intentions are mostly tied with make commitment, but at that place is an important difference betwixt them. Brand commitment refers to the connectedness a consumer establishes with a brand, whereas repeat purchase is the purchase of a make because it is relatively cheaper [65].

Loyal customers are the faithful consumers of a brand who perform repeat purchases and recommend the make to those around. Firms want their customers to be attached to their brands by strong feelings. Client satisfaction must exist fulfilled for this kind of loyalty. When customers are satisfied, they show commitment to continually buy the same brand and become loyal [66].

Consumers who are committed to the brand become loyal consumers and show consistent repurchase beliefs [67]. Therefore, loyalty may touch on consumer repurchase behaviors [68]. Repurchase intentions are usually identified through brand commitment, just there are significant differences between the 2 concepts. Brand commitment ways a similar human relationship to the attachment that consumers develop for the brand. Therefore, in this study, the following research hypothesis is established based on previous studies:

Hypothesis 3(H3).

Emotional Loyalty Positively Impacts Intention to Repurchase.

Enquiry on habits is important for consumer beliefs because repetition is a central characteristic of daily life. Near 45% of people'due south behavior is repeated almost daily and usually in the same context [69,70].

Prior inquiry comparing TRA and related theories with habit as an ancestor of behavioral intentions showed that habit directly affects behavioral intentions [71,72,73]. Gefen (2000) noted that habitual previous preference to use an online shopping website direct and strongly increased user intentions to continue using the same online shopping website again [74]. Support for the role of habit in repeat purchase intention was provided by Gefen (2000) and Rauyruen and Miller (2009) [74,75].

Habitual behavior exhibits that repurchase is motivated by habit or routines that are facilitated in the decision-making process [76].

Khare and Inman (2006) realized that consumers either buy the same brand repeatedly or only try new product types within the same brand, depending on the situation [77]. Research also institute various types of habitual purchase patterns, such as purchasing diverse trademarks habitually according to one'due south values [77]. Consumers tend to buy the same brands of products across different shopping experiences (e.g., Seetharaman, 2004) [78], purchase the same amounts at a given retail shop beyond repeat visits [79], and eat similar types of foods at a meal each twenty-four hour period (east.g., Khare & Inman, 2006) [77]. Thus, repetition—and, more specifically, habits—may characterize a significant segment of consumer behavior that tin can be linked to important marketing outcomes [38].

Anshari et al. (2016) examined the effect of habit on smartphone usage [80]. They found that in that location is a strong relationship between habit and smartphone usage. As there is a positive issue of addiction on consumption behavior and smartphone usage, nosotros think that there may also be a similar i between make loyalty and re-buy intention. Therefore, in this study, the following research hypothesis is established based on previous studies:

Hypothesis 4(H4).

Consumer Habit Positively Impacts Intention to Repurchase.

4.2. Research Model

Previous inquiry analyses suggested the research model and its components shown in Figure 3. To examine the experiences of consumers using the same brand of smartphone, the research model was developed based on 5 factors (consumer satisfaction, social influence, emotional loyalty, habit, and intention to repurchase). Relying on the research model, the analysis examined the furnishings of social influence, consumer satisfaction, emotional loyalty, and habits on the intention to repurchase smartphones.

five. Methodology for Data Collection, Data Analysis, and Measurement

5.one. Data Drove and Sample Size

To analyze the enquiry model, this study collected data on intention to repurchase through surveys for consumers living in Republic of korea. Co-ordinate to the Pew Research Center (2018), a global market research organization, the country with the highest smartphone penetration rate is South Korea, with 94% of the population using smartphones [81]. Therefore, Southward Korea is a practiced market in which to clarify the characteristics of consumers who repurchase smartphones.

Although questionnaires were distributed to 1200 customers, only 390 responses were received and analyzed in order to test the hypotheses, as now mentioned in Section v.1. Three hundred ninety consumers (northward = 390) who repurchased smartphones within two years were included in this investigation. The study tested the inquiry hypotheses and attempted to answer the research questions by developing a questionnaire as its research musical instrument. The items of each construct were adopted from previous literature. All of the items were measured on a five-indicate Likert scale, where 1 = strongly disagree and 5 = strongly agree. This study calculated the appropriate sample size for the assay inside the level statistical significance; in consideration of the current full population of South Korea, the smartphone penetration rate, the confidence level (95%), and the margin of error (5%), the appropriate sample size could exist calculated as 385 people. Therefore, the sample (n = 390) in this study is an advisable size for data analysis.

5.two. Measurement

This report analyzed the behavior of consumers; for this, the research used verified measurement items found in an analysis of previous research. And the results were summarized in Table 2.

In this study, 33 items were developed for the questionnaire to address the characteristics of the consumers. The last questionnaire had 17 questions, developed through a pilot test of 78 consumers.

five.three. Assay

The descriptive statistics of the survey were analyzed using SPSS 24.0 to make up one's mind the means and standard deviations of the responses. A factor analysis and a reliability assay (Cronbach's α test) were conducted to analyze the validity and the reliability of the research variables. To clarify the causal relationship betwixt the variables, the regression analysis model was enhanced by artificial neural network algorithms.

6. Data Analysis

6.i. Descriptive Statistics

In this report, the results of the descriptive statistics of the survey are presented in Table 3.

Table iv presents the results that explain the characteristics of the respondents who participated in the survey. Male (49.5%) and female (fifty.5%) respondents participated in this survey in an equal ratio, and consumers who repurchased smartphones 2–v times in the final two years deemed for more than 80% of all respondents.

6.2. Factor Analysis

In this report, factor assay was based on the nerveless data. For factor analysis, maximum likelihood was used as the cistron extraction method, and oblimin with Kaiser normalization was used as a factor rotation method [89,90]. In improver, cistron analysis indicated that 17 observed variables could be clustered into five latent variables. The results of the cistron analysis in this study were validated through the Kaiser–Meyer–Olkin (KMO) test and Bartlett's test. The result of the KMO examination was 0.889, which suggests that the factor analysis was appropriate (Table 5).

Based on consumer information, the results of factor analysis included survey results grouped into v factors, and the reliability of the elements that course each cistron was adamant to exist excellent. The results appear in Tabular array 6.

6.3. Correlation Analysis

This written report analyzed the directionality of the factors through correlation analysis between the derived factors, as shown in Table seven.

6.4. Regression Analysis

Regression analysis was performed to analyze the linear causality between several independent and dependent variables in this report. The results are shown in Tabular array 8, Table 9 and Tabular array 10. The analysis was based on the stepwise method and was analyzed using SPSS 24.0.

Co-ordinate to the results in Table ix, the second research model was analyzed to be highly consummate, and the regression analysis results of this model are presented in Table 9.

Table 10 shows the results of analyzing the coefficients of the regression assay model variables.

half dozen.5. Assay of Research Model Using ANN (Relative vii:3, Number of Hidden Layers (Ane))

This study enhanced the analysis of the research model by using the ANN algorithm. To this end, with one hidden layer, 2 cases were analyzed separately. Table 11, Table 12, Tabular array thirteen, Table 14 and Table 15 are the results of analyzing the inquiry model bold one hidden layer. With the ANN algorithm, this study advanced the analysis of the research model.

Table 12 presents material that explains the method of analyzing research models with ANN. In the report presented here, a research model consisting of iv independent variables and ane dependent variable was subjected to multiple regression assay. In addition, the sigmoid office, which combines the node and weight of the hidden layer when transmitted from raw data to the hidden layer, was used as an active function in ANN. Beyond that, the softmax part was selected every bit an activation part for calculating the results from the hidden layer to the output layer.

In this research, when the number of hidden layers was ready equally ane under the ANN algorithm, the third research model was found to be the most complete model.

Model 3 (Table 16) determined that consumer satisfaction (0.621), emotional loyalty (0.125), and social influence (0.063) influenced intention to repurchase, unlike the other models. In add-on, it determined that consumer satisfaction had the greatest influence on intention to repurchase.

6.vi. Analysis of Enquiry Model Using ANN (Relative seven:three, Number of Hidden Layers (Two))

Table 17, Table eighteen, Table nineteen, Tabular array 20, Table 21 and Table 22 are the results of analyzing the research model by bold ii hidden layers in the ANN algorithm.

The analysis results of Table 21 show that the third research model was the most consummate when the number of subconscious layers was set to two under the ANN algorithm.

Model iii (Table 22) determined that consumer satisfaction (0.710), emotional loyalty (0.108), and social influence (0.062) influenced repurchase intention, unlike the other models. In addition, information technology determined that consumer satisfaction had the greatest influence on repurchase intention.

vii. Research Results

This study plant that consumer satisfaction positively affects intention to repurchase. In addition, it was adamant that emotional loyalty affects intention to repurchase. According to the multiple regression analysis, but customer satisfaction and emotional loyalty bear on intention to repurchase. In the original analysis, social influence did not affect intention to repurchase. This is due to the limit of this study, in that it did non consider the relationship betwixt each independent variable. Therefore, the stepwise analysis of variables was limited. To address these limitations, this study more than clearly analyzed the causal human relationship between variables using the ANN algorithm.

This newspaper analyzed the validity and validity of the research hypotheses through statistical assay. The results of the assay are summarized in Table 23. In this written report, the enquiry model in Figure 3 was analyzed by using regression analysis and the ANN algorithm, as summarized in Table 24. The results of this approach showed that the analysis model (root-mean-foursquare error (RMSE) (0.313)) was almost complete with 2 subconscious layers in the ANN algorithm—that is, the third model in Table 24 was the research model with the highest completeness. In other words, the inquiry hypotheses (H1, H2, and H3) were accustomed in this report when analyzed using the ANN algorithm. In this model, the coefficients of consumer satisfaction, emotional loyalty, and social influence were improved compared to the first and second research models. This paper analyzed the validity and validity of the enquiry hypotheses through statistical analysis. The results of the analysis are summarized in Tabular array 23.

8. Research Implication

eight.ane. Theoretical Implication

This study introduced the TRA (Theory of Reasoned Action) and Heuristic theory, which are effective theories to explain consumer beliefs. The results of this study seem to be very simple; however, this arroyo studied consumer behavior from a unlike perspective than other studies. The result of this report was a deviation from the rational aspect of consumer behavior research on the premise of reason to the irrational attribute based on emotion, highlighting the necessity of expanding consumer behavior research. In other words, this is a novel exploration not found in previous studies and is a meaningful research consequence that can expand the scope of research with differentiation from existing studies. Nonetheless, it was not institute that the heuristic variable, a variable of irrational concept, plays an of import role among the variables of TRA. On the other manus, social influence was plant to affect consumer purchasing beliefs. The concept of TRA is based on rational reason, but the results of this study explain that the TRA should be expanded. In other words, when social influence was added to the existing TRA variables, it was found that a more than explanatory enquiry model was completed through the ANN algorithm. This is the result of the written report that suggested the possibility of overcoming the theoretical limitations of the TRA'due south lack of explanation ability by measuring social influence variables every bit important variables among the TRA'south variables. In other words, this is an analysis result that tin infer the necessity of expanding the premise of rational reason equally an additional test result that finer explains consumer choice behavior.

viii.ii. Managerial Implication

So far, companies only introduce a fragmented customer strategy, such as purchasing products at discounted prices, when they repurchase the aforementioned brand products as those used to appointment, to encourage customers to repurchase. Today, however, customer tendencies and characteristics are more diverse. Therefore, intention to repurchase should also be studied from various perspectives.

This paper found that consumer satisfaction, emotional loyalty, and social influence accept a direct bear on on consumer intentions to repurchase a smartphone. However, the consequence of social influence on intention to repurchase was found to exist insignificant.

If the product is expensive (like a smartphone), analysis showed that the consumer uses the product and repurchases the product if satisfied. Therefore, in order to motivate consumers to repurchase smartphones, companies should provide consumers with positive information near whatever new features and the utilization value of the smartphone. This data can raise the consumer'southward usability of the product, which positively affects satisfaction with the product. Therefore, companies need to consider a number of strategies to increment consumer intentions to repurchase smartphones. Primarily, consumer satisfaction needs to exist improved past demonstrating differentiated functions of smartphones and the convenience and services they provide. Information that can amend the usability of the production, equally well as data about its superior quality, affects the satisfaction of consumers. This has a positive effect on repurchase.

Therefore, companies demand to consider several strategies to enhance customer intention to repurchase smartphones. Firstly, they might ameliorate client satisfaction by enhancing the functions of smartphones and the convenience and services that they provide. Secondly, it is necessary for them to provide positive information to customers through social influence such as word of mouth.

8.3. Differentiation from Previous Research

Previous studies focused on models of repurchase intention using client loyalty and customer satisfaction every bit contained variables. Moreover, several researchers identified satisfaction and attitude every bit major antecedents of customer repurchase intention [91,92]. Co-ordinate to such research, satisfaction is the overall level of a customer'south pleasure and delectation resulting from experience with the service. However, the precise human relationship between customers' learned dispositions and preferences for perceived alternatives remains unclear [93].

This paper discussed consumer repurchase intention by considering social influence, customer satisfaction, emotional loyalty, and customer habit. Differences between past research and this paper include the following:

• This study examined whether customer habits direct affect their repurchase intention;

• Marketing strategies for repurchase customers tin can differ from those for other competitors;

• This study involved analyzing factors of social influence that directly affect repurchase intention.

9. Inquiry Limitation and Further Study

This study analyzed the research hypotheses by collecting survey data from consumers in Republic of korea. However, bias in consumer analysis can cause bug when data are only collected from consumers in certain countries, which tin limit the objectivity of the research model. Therefore, in the future, it will be necessary to further analyze the research hypotheses by expanding the survey to consumers living in the United states and elsewhere.

In addition, information technology is necessary to study the first-time smartphone purchase intention of consumers (not repurchase) and compare those results with the results of this study. In addition, it is necessary to deeply analyze and study all factors that touch consumer satisfaction with regard to smartphones.

Funding

This work was supported past a Kyonggi Academy Research Grant 2019.

Conflicts of Interest

The author declares no conflict of involvement.

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Figure 1. Multilayer perceptron structure.

Figure ane. Multilayer perceptron structure.

Figure 2. Single neuron.

Figure 3. Inquiry model.

Effigy iii. Enquiry model.

Table 1. Intention to repurchase by smartphone brand [i].

Table 1. Intention to repurchase past smartphone brand [ane].

		Case	Intention to Repurchase past Smartphone Make
			Samsung Milky way		Apple tree IPhone	LG G/5/Ten	Other	Respond Refusal
			S/A/J	Note
Currently Used Smartphone Brand	Samsung Galaxy S/A/J	423	61%	4%	four%	seven%	ane%	24%
	Milky way Annotation	137	6%	67%	6%	v%	1%	fifteen%
	Apple iPhone	161	nine%	three%	77%	iv%		eight%
	LGG/5/Ten	153	xi%	iv%	iii%	47%	0%	34%

Table 2. Measurement items.

Table two. Measurement items.

Construct	Measurement Items	Related Studies
Social Influence	Search for information virtually diverse mobile telephone brands and models from the Professional Association or an independent group of experts Collect mobile phone information from friends, neighbors, relatives, or coworkers (east.g., how the performance of brand A jail cell phone is compared to brand B) My choice of cell telephone is influenced by other consumers' word of mouth or some evaluation reports from an independent testing agency (e.g., online prison cell telephone reviews) My friends' assessments and preferences bear upon my choices My family members' preferences affect my cell phone choices My decision to buy a cell phone is influenced by my classmates' and colleagues' preferences, to meet their expectations Using a jail cell phone of a detail brand or model helps show others who I am or who I desire to be (successful businessman, female expert, etc.)	[82,83]
Consumer Satisfaction	Satisfaction with the current smartphone The current smartphone meets all reasonable requirements Loftier satisfaction with the quality of my smartphone Smartphone meets my expectations The function and blueprint of my smartphone is appropriate for requirements The office and the design of my smartphone fit my needs/wants Feeling better with smartphone use My decision to use my smartphone was a wise one	[84,85,86]
Emotional Loyalty	Brand love has a positive touch on willingness to pay a toll premium Dearest this brand Passionate about this brand Very attached to this brand Willing to pay a premium cost to buy this make of smartphone	[87]
Habit	Tend to like routine Rely on what is tried and tested rather than explore something new I prefer to work happily in my comfort zone rather than challenging myself Tend to do things in the same gild every morning (east.g., get upwards, go to the bathroom, accept a coffee, etc.) Always sit in the same seat in places such as on the motorcoach, in the movie house, or in church building Sometimes there is a temptation to go to special events, but I generally purchase a reliable habitation brand If I could, I would probably ever choice the same outfit to wear every twenty-four hours I always like to park my motorcar or bicycle in the same identify I tend to stick with the familiar version of the software parcel as long as possible E'er tend to visit the same websites when browsing the internet In a restaurant, I tend to gild dishes that I am familiar with	[88]
Intention to Repurchase	I intend to continue purchasing the same smartphone in the future I volition proceed with the same smartphone in the future I will regularly use the aforementioned smartphone in the futurity	[84,85,86]

Tabular array 3. Descriptive statistics.

Table 3. Descriptive statistics.

	n	Minimum	Maximum	Mean		Standard Deviation	Variance
				Statistic	Standard Error
Q4	390	ane	5	3.22	0.056	1.039	1.079
Q5	390	one	5	3.ten	0.060	ane.114	1.240
Q6	390	1	5	3.09	0.060	ane.110	1.233
Q8	390	1	five	3.82	0.042	0.786	0.617
Q9	390	1	v	3.55	0.045	0.842	0.710
Q10	390	1	five	three.68	0.043	0.808	0.652
Q11	390	ane	5	3.64	0.042	0.783	0.613
Q13	390	1	v	3.66	0.042	0.790	0.624
Q14	390	1	5	3.56	0.042	0.792	0.627
Q15	390	i	5	iii.65	0.042	0.783	0.614
Q18	390	1	5	3.fifteen	0.048	0.893	0.798
Q19	390	1	5	2.65	0.050	0.934	0.873
Q20	390	one	5	2.57	0.052	0.965	0.931
Q25	390	ane	v	3.03	0.054	1.001	1.002
Q28	390	i	5	2.98	0.054	1.003	1.005
Q29	390	1	five	three.18	0.052	0.972	0.944
Q33	390	i	5	iii.35	0.043	0.799	0.638

Table four. Analysis of survey respondents.

Table four. Analysis of survey respondents.

Gender
	Frequency	Per centum	Valid Pct	Cumulative Percentage
Man	193	49.5	49.5	49.5
Woman	197	fifty.v	50.5	100.0
Total	390	100.0	100.0
Age
	Frequency	Percentage	Valid Pct	Cumulative Percent
twenty southward	112	28.vii	28.7	28.7
thirty due south	177	45.iv	45.four	74.1
40 s	52	13.3	13.3	87.iv
l s	49	12.half-dozen	12.6	100.0
Total	390	100.0	100.0
Number of Smartphone Repurchases
Number of smartphone repurchases	Frequency	Percentage	Valid Pct	Cumulative Percentage
2	29	7.5	7.5	7.v
3	91	23.three	23.3	30.7
iv	90	23.0	23.0	53.vii
5	105	27.0	27.0	80.7
half dozen	33	8.3	8.3	89.1
vii	9	2.3	2.iii	91.4
8	6	1.iv	1.iv	92.8
nine	ane	0.three	0.3	93.1
10	20	5.ii	5.2	98.three
12	one	0.three	0.three	98.6
15	three	0.9	0.ix	99.4
sixteen	1	0.3	0.three	99.7
17	one	0.three	0.three	100.0
Total	390	100.0	100.0

Table five. Kaiser–Meyer–Olkin (KMO) and Bartlett'south tests.

Table five. Kaiser–Meyer–Olkin (KMO) and Bartlett's tests.

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.889
Bartlett's Test of Sphericity	Guess chi-foursquare	5406.133
	df	528
	Sig.	0.000

Table 6. Cistron analysis.

Table 6. Factor analysis.

		Gene					Cronbach's Blastoff
		1	ii	3	iv	6
Client Satisfaction	Q11	0.905					0.896
	Q10	0.883
	Q9	0.831
	Q8	0.689
Social Influence	Q6		0.871				0.784
	Q5		0.716
	Q4		0.618
Habit	Q29			0.709			0.708
	Q28			0.661
	Q25			0.612
Emotional Loyalty	Q20				0.817		0.8
	Q19				0.745
	Q18				0.670
Intention to Repurchase	Q14					0.808	0.848
	Q15					0.773
	Q13					0.715
	Q33					0.604

Tabular array 7. Correlation analysis.

Table 7. Correlation analysis.

		Social Influence	Emotional Loyalty	Intention to Repurchase	Customer Satisfaction
Social Influence	Pearson correlation	1	0.327 **	0.196 **	0.182 **
	Sig. (2-tailed)		0.000	0.002	0.001
	northward	390	390	390	390
Emotional Loyalty	Pearson correlation	0.327 **	1	0.515 **	0.397 **
	Sig. (2-tailed)	0.000		0.000	0.000
	due north	390	390	390	390
Intention to Repurchase	Pearson correlation	0.169 **	0.467 **	one	0.728 **
	Sig. (2-tailed)	0.002	0.000		0.000
	northward	390	390	390	390
Client Satisfaction	Pearson correlation	0.182 **	0.397 **	0.728 **	1
	Sig. (2-tailed)	0.001	0.000	0.000
	n	390	390	390	390

Table 8. Regression analysis.

Table viii. Regression analysis.

Model	Variables Entered	Variables Removed	Method
one	Client satisfaction		Stepwise (criteria: probability of F to enter ≤ 0.050, probability of F to remove ≥ 0.100).
ii	Emotional loyalty

Table 9. Model summary.

Model	R	R Square	Adjusted R Square	Standard Fault of the Guess	Change Statistics					Durbin–Watson
					R Square Change	F Modify	df1	df2	Sig. F Change
1	0.728 a	0.530	0.529	0.47442	0.530	390.500	1	346	0.000
2	0.753 b	0.568	0.565	0.45575	0.038	29.934	1	345	0.000	ane.869

Tabular array 10. Coefficients ^a.

Table 10. Coefficients ^a.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Standard Error	Beta
1	(Abiding)	0.996	0.135		7.360	0.000
	Customer satisfaction	0.716	0.036	0.728	19.761	0.000
2	(Constant)	0.780	0.136		5.743	0.000
	Customer satisfaction	0.634	0.038	0.645	16.715	0.000
	Emotional loyalty	0.185	0.034	0.211	5.471	0.000

Table 11. Example processing summary.

Table 11. Case processing summary.

		n	Percentage
Sample	Training	283	72.75%
	Testing	106	27.25%
Valid		389	100.0%
Excluded		one
Full		390

Table 12. Network information. MLP—multilayer perceptron.

Table 12. Network information. MLP—multilayer perceptron.

Input Layer	Factors	i	Client satisfaction
		ii	Habit
		3	Social influence
		4	Emotional loyalty
	Number of units		51
Subconscious Layer(s)	Number of hidden layers		1
	Number of units in hidden layer 1a		8
	Activation function		Sigmoid
Output Layer(south)	Dependent variables	1	Predicted value for MLP predicted value
	Number of units		half dozen
	Activation function		Softmax
	Error role		Cantankerous-entropy

Tabular array 13. Model summary ^a.

Table 13. Model summary ^a.

Training	Cross-entropy error	13.233
	Percent incorrect predictions	0.0%
	Stopping rule used	ane consecutive step(due south) with no decrease in error a
Testing	Cross-entropy mistake	50.596
	Percentage wrong predictions	seven.4%

Tabular array fourteen. Regression analysis.

Tabular array fourteen. Regression analysis.

Model	Variables Entered	Variables Removed	Method
1	Customer satisfaction		Stepwise (criteria: probability of F to enter ≤ 0.050, probability of F to remove ≥ 0.100).
2	Emotional loyalty
three	Social influence

Table 15. Model summary.

Model	R	R Foursquare	Adapted R Square	Standard Error of the Gauge	Modify Statistics					Durbin–Watson
					R Square Change	F Change	df1	df2	Sig. F Modify
1	0.814 a	0.663	0.663	0.34972	0.663	1429.476	1	726	0.000
2	0.831 b	0.690	0.690	0.33463	0.028	65.913	1	725	0.000
3	0.835 c	0.696	0.696	0.33155	0.006	14.557	ane	724	0.000	1.869

Table xvi. Coefficients ^a.

Table 16. Coefficients ^a.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Variance Inflation Factors (VIF)
		B	Standard Error	Beta
1	(Abiding)	1.128	0.070		16.190	0.000
	Client satisfaction	0.698	0.018	0.814	37.808	0.000	1.000
2	(Constant)	0.892	0.073		12.260	0.000
	Customer satisfaction	0.636	0.019	0.742	32.982	0.000	1.188
	Emotional loyalty	0.145	0.018	0.183	8.119	0.000	1.188
3	(Constant)	0.805	0.076		10.646	0.000
	Customer satisfaction	0.621	0.020	0.724	31.832	0.000	ane.238
	Emotional loyalty	0.125	0.018	0.158	6.785	0.000	one.290
	Social influence	0.063	0.017	0.086	3.815	0.000	1.211

Table 17. Instance processing summary.

Tabular array 17. Example processing summary.

		northward	Percentage
Sample	Training	283	72.75%
	Testing	106	27.25%
Valid		369	389
Excluded		i	1
Full		370	390

Table 18. Network information.

Table xviii. Network information.

Input Layer	Factors	1	Customer satisfaction
		2	Habit
		3	Social influence
		iv	Emotional loyalty
	Number of units		48
Hidden Layer(s)	Number of hidden layers		2
	Number of units in hidden layer 1 a		ix
	Number of units in subconscious layer 2 a		7
	Activation function		Sigmoid
Output Layer(southward)	Dependent variables	1	Predicted value for MLP predicted value
	Number of units		v
	Activation function		Softmax
	Fault role		Cantankerous-entropy

Table 19. Model summary.

Training	Cross-entropy fault	13.272
	Percentage wrong predictions	0.0%
	Stopping rule used	1 consecutive step(south) with no subtract in error a
Testing	Cross-entropy error	thirty.578
	Percentage incorrect predictions	half dozen.8%

Table 20. Regression analysis.

Tabular array xx. Regression analysis.

Model	Variables Entered	Variables Removed	Method
1	Client satisfaction		Stepwise (criteria: probability of F to enter ≤ 0.050, probability of F to remove ≥ 0.100).
2	Emotional loyalty
iii	Social influence

Table 21. Model summary.

Model	R	R Square	Adjusted R Square	Standard Fault of the Estimate	Change Statistics					Durbin–Watson
					R Foursquare Change	F Change	df1	df2	Sig. F Change
1	0.843 a	0.710	0.710	0.32800	0.710	1754.999	ane	726	0.000
2	0.855 b	0.731	0.731	0.31626	0.021	55.218	1	725	0.000
3	0.859 c	0.737	0.737	0.31310	0.006	15.515	1	724	0.000	1.869

Table 22. Coefficients ^a.

Table 22. Coefficients ^a.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Standard Mistake	Beta
ane	(Constant)	0.836	0.071		eleven.801	0.000
	Customer satisfaction	0.783	0.019	0.843	41.893	0.000
2	(Constant)	0.646	0.073		8.868	0.000
	Client satisfaction	0.724	0.020	0.779	36.741	0.000
	Emotional loyalty	0.128	0.017	0.158	seven.431	0.000
3	(Abiding)	0.558	0.076		7.380	0.000
	Client satisfaction	0.710	0.020	0.764	35.834	0.000
	Emotional loyalty	0.108	0.018	0.133	6.070	0.000
	Social influence	0.062	0.016	0.083	iii.939	0.000

Tabular array 23. Analysis and comparison of research models.

Table 23. Analysis and comparison of research models.

Enquiry Hypothesis	Research Model No. (1)	Research Model No. (2)	Research Model No. (3)
Consumer satisfaction positively impacts intention to repurchase (H1)	Accept	Accept	Accept
Social influence positively impacts intention to repurchase (H2)	Reject	Accept	Accept
Emotional loyalty positively impacts intention to repurchase (H3)	Accept	Accept	Accept
Consumer addiction positively impacts intention to repurchase (H4)	Decline	Turn down	Reject

Table 24. Analysis and comparing of research models. RMSE—root-mean-square error.

Table 24. Analysis and comparing of research models. RMSE—root-mean-square error.

Inquiry Model No.	1	2	3
Analysis method	Regression analysis	Regression analysis (number of hidden layers (ane))	Regression analysis (number of hidden layers (2))
	R Square (0.568)	R Foursquare (0.696)	R Square (0.736)
	RMSE (0.456)	RMSE (0.332)	RMSE (0.313)
(Constant)	0.780	0.805	0.558
Satisfaction	0.634	0.621	0.710
Emotional loyalty	0.185	0.125	0.108
Social influence	-	0.063	0.062

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