1. Siddhant Institute of Business Management, Pune, Maharashtra, India; pra_aashish@gmail.com
2. S.N.G. Institute of Management & Research, Pune, Maharashtra, India; r_ kumar32@gmail.com

*             Correspondence: r_ kumar32@gmail.com

Keywords: purchase intention, purchase behavior, online service users, internet

1. INTRODUCTION

These days, the Internet has become an integral part of our day-to-day lives, playing a role in everything from communication with our society to the purchase and sale of goods and services. E-markets have become increasingly popular in recent years, surpassing the use of traditional marketplaces. Online shopping has been gaining popularity over the past few years [1], and this trend is expected to continue. As a consequence of this, businesses are increasingly interacting with their customers through online and so-called “omni channels.” A recent estimate by Forrester suggests that the amount of money spent on goods purchased from online retailers in the United States alone surpassed $334 billion in 2015. Online sales are projected to reach 680 billion dollars by the year 2024, representing a compound annual growth rate (CAGR) of 10 percent over the next five years. This indicates that online shopping has achieved a significant level of annual growth. Customers are increasingly willing to pay a little bit more and switch to online shopping, also known as the “buying of Digital Goods” [2]. This shift in consumer behavior is a reflection of the shifting fashion trends. The increasingly hectic lifestyles of today’s consumers, combined with the proliferation of easily accessible alternative brands for a diverse array of goods and services, have contributed to the rise in popularity of online shopping. According to the findings of research [3, consumers make purchases online for a number of different reasons. According to the findings of a study conducted by Swinyard and Smith, consumers may choose to shop for a service or product online because they want delivery right to their doorstep and believe their purchase will remain private. Researchers basically carried out studies to investigate the lifestyle characteristics of online buyers [4]. According to studies conducted in the past, online shoppers have a tendency to have different preferences than offline shoppers in terms of the amount of product information, variety, and customization they desire in the goods or services they buy. According to the Big Model Theory’s working hypothesis, there is a significant divide between the growing trend of online shopping and the typical consumer. Additionally, there are academics who believe that traditional retail stores face a significant threat from E-Commerce, also known as online shopping [5]. This research used the “Unified Theory of Acceptance and Use of Technology” (UTAUT 2) model, with the addition of Social Influence as a moderator on the path of Purchase Intention and Purchase behavior, in order to fill the gap that existed for online purchasing behavior for a service. This research was conducted as part of the present research. The “Technology Acceptance Model,” known as the UTAUT, was developed by Venkatesh and colleagues [6]. The UTAUT framework is essentially an expansion of the TRA Theory of Reasoned Action and the TAM Technology Acceptance Model.

2. LITERATURE REVIEW

The ease of use that is associated with a consumer’s interaction with technology is referred to as effort expectancy. The term “effort expectancy” refers to a technique that gives the impression that information technology is simple and straightforward to use [7]. A significant study was done on the topic of online shopping for older adults in Taiwan, and the authors came to the conclusion that effort expectations in Taiwan will have a positive influence on the intention of older adults to engage in online shopping. The phrase “the degree to which using a technology will provide benefits to consumers in performing certain activities” is how performance expectancy is defined. A technique known as performance expectancy is one that, when applied, can make the utilization of technology advantageous for the user when carrying out specific kinds of activities [8]. A significant study was carried out on the topic of online shopping for older adults in India, and the authors came to the conclusion that performance expectations in India will have a positive impact on the intention of older adults to engage in online shopping [9].Moving towards a website design that is already well-established will be helpful in attracting customers who are interested in our products and services [10]. It’s a pretty simple way to get our customers interested in a product and change their approach to online shopping. According to the findings of this study, improving and changing the impulsive behavior of consumers can be accomplished through motivating, timing, perceived responsibility, and social relevance. There is a possibility that social responsibility and relevance will have an effect on the disposition of the customer, which will ultimately result in a more favorable attitude toward online shopping [11]. On the other hand, it should be noted that social references can also affect a person’s disposition and level of confidence when shopping online. It is possible to assert that reference groups have an impact on the behavior of consumers as well. If they are told about a product by their family, friends, or any other acquaintance, it will unquestionably have an effect on the sale of a particular brand. The main idea here is that reference groups do have an effect on the value of brands as well as consumer behavior. When developing their products, companies should keep the preferences, interests, and needs of their target audiences in mind [12].Regardless of whether a purchase is made offline or through an online buying medium, the decision-making process and shopping behavior are, to some extent, the same. However, there are still distinctions to be found in the form of the shopping environment that is produced by the brand or their advertising. The circle of consumer decision behavior obviously begins with the recognition of a need, followed by the gathering of the necessary information, the consideration of available alternatives, the finalization of choice, and, of course, the demonstration of post-purchasing behavior [13]. Online communication is the advertising of a product through the use of advertisements, promotions, or banners that are designed to get the attention of customers and convince them to purchase a particular product. On the other hand, prior to making the purchase, they are required to obtain some additional information about the product that will assist them in making a decision regarding which option to go with. In the event that the advertisement does not contain such information, the customer will attempt to locate it through various online mediums, such as catalogs, websites, and so on [14].It has grown in popularity in the realm of company development, and the number of businesses operating solely online is continually on the rise. Despite this, a significant portion of these online shops is unsuccessful. According to research [15], one of the most compelling reasons why these online stores are unsuccessful is that customers do not trust them and have a perception that they are putting themselves in harm’s way. According to the opinions of various experts, customers who shop online are vulnerable to four different types of threats. Customers’ perceptions of the level of risk involved in making a purchase decision are a major factor in whether or not they choose to make their purchases online in the first place. On the other hand, there was no additional evidence of a link between the perception of uncertainty and the intention to make a purchase [16].The social presence of an online retailer is what inspires initial trust in that retailer. The presence of social media will have an effect on sales and purchases, as well as on the level of customer satisfaction and the practicality of online shopping. The customers’ level of trust in one another is an essential component of successful online shopping [17]. The term “trust” is used to refer to individuals’ or persons’ views or anticipating others’ ethical actions to stay under influencing elements such as subjective standards, security, risk, confidence, and so on. Researchers look to individuals like these influentials in order to determine people’s natural inclination to trust others. They learn to trust one another, which is an essential role in any and all forms of interaction. The majority of the earlier studies [18] explain how trust is related to risk. The level of trust that customers have in their online shopping experiences is a significant factor in determining whether they prefer to shop at e-tailers or e-marketplaces. Further investigation revealed that there was a causal connection between all five types of perceived risks. Where only the performance risk of the product was found to be positively related to the trust expectations of consumers. It’s possible that the customer’s perception of the product’s performance has a direct bearing on how much risk they take in an online transaction. [19]. Nowadays, the core and critical research topic is related to the understanding of customer purchase intention and behavior related to the use and acceptance of new technology [20]. The Theory of Reasoned Action (TRA), Diffusion of Innovation Theory, Theory of Planned Behavior (TPB), and DeLone and McLean’s success model are some of the other models that have been used. Venkatesh et al. reviewed earlier work on user behavior and IT acceptance in 2003 and established the Use of Technology (UTAUT) and the Unified Theory of Acceptance [21]. In the context of E-Buying of tangible items, the base article, as well as several prior research have been undertaken without the moderating of social influence between Purchase Intention and Purchase Behavior. By researching the behavior of service purchasers over the Internet, this study aims to address this vacuum in knowledge.                The present research is going to check the impact of four independent variables, i.e., Performance Expectancy, Perceived Risk, Effort Expectancy, and Perceived Trust on Purchase Intent, and also analyze the moderating impact between Purchase Intention and Purchase Behavior for online service buyers of India.

H1: There is an impact of effort expectancy on Purchase Intention.

H2: There is a positive impact of performance expectancy on Purchase Intention.

H3: There is a positive impact of perceived risk on purchase Intention.

H4: There is an impact of perceived trust on purchase Intention.

H5: There is a moderating impact of social influence on the relationship between purchase Intention and Purchase behavior.

H6: There is no moderating impact of social influence on the relationship between purchase Intention and Purchase behavior.

 

Figure 01: Conceptual Model

3. MATERIALS AND METHODS

The target population of this study was N=300 students who belonged to different Business institutes in India. The response rate was 80% who buy through online buying mediums. A convenience-based sampling technique has been used. This study has been conducted by using a close-ended 5-point Likert scale questionnaire. An instrument containing 30 questions has been adopted from [22], and distribution was that Effort Expectancy(4 Indicators), Performance Expectancy (5 Indicators), Perceived Risk (7 Indicators), Perceived Trust (8 Indicators), Social Influence (2 Indicators), Purchase intention (2 Indicators) and Social Behaviour (2 Indicators). The instrument has been used by Felix et al. [23]. After the first filtration, 240 respondents met our requirement of buying any service online; after omitting error responses, we have a total sample of 220 respondents. SPSS v. 16 and SmartPLS (v. 3.2.6) were used for data analysis and hypothesis testing. In a study by Ringle, Wende & Becker (2015), the analysis of hypotheses was done using Smart PLS 3, the most recent version of Partial Least Square-Structure Equation Modeling. PLS-SEM is a multivariate research methodology that uses statistical tools to concurrently evaluate several variables. Using Smart PLS 3.0, the data was thoroughly checked. An approach to establishing the validity of Indian notions was to employ composite dependability. To demonstrate convergent validity, factor loading values for valid constructs should be more than 0.7. Convergent validity is the degree to which all numerous items of parts of the model are utilized to assess their own construct. For this, we examine outer loading and variance extracted [24]. The value of each element must be bigger than 0.7 in order to fulfill the factor loading criterion. According to Hair et al. [25], the acceptable factor loading value is that value should be larger than 0, but it is useful as an indication when it exceeds 0.7 according to the authors of the study. This table provides a breakdown of the factor loadings for each variable. Only a small number of our values (Effort Expectancy, Risk Perceived, and Trust Perceived) fall below the threshold of 0.7. Therefore we ignore those items for further analysis

4. RESULTS AND DISCUSSION

To determine the reliability of constructions, composite reliability, and Cronbach’s Alpha value were utilized. Its value should be more than 0.7. Cronbach’s alpha and composite reliability were used to assess the internal consistency and reliability of the measurements for each model construct. In the current research, values of every variable reach the threshold value except one value of purchasing behavior. However, Composite reliability indicates reliable findings for it as well. Standard Deviation Extracted is another statistic that can be used to demonstrate convergent validity. AVE is a degree that demonstrates the fact of convergent validity. According to [26] AVE value for each concept should be higher than 0.5, which reflects the quality of convergent validity. In the current article, values are in the range of (0.6464 – 0.8714), which demonstrates all the values are up to mark.

Table 01: Convergent Validity and Reliability

Discriminant validity shows distinct concepts of items and their constructs; it is basically the square root of the Average Variance Extracted. To measure this, we use Fornell-Larcker Criterion and cross-loading. The value of Fornell-Larcker should be greater than 0.6 of all variables and should be lesser than others. So, values that are greater than 0.6 show valid results. Table 2 shows the Fornell-larker criterion of research; all the values met the threshold criteria, so all are good enough.

Table 02: Fornell-Larcker Criterion

Cross Loading is mostly utilized to promote discriminant validity by displaying the factor loading value of one indicator with respect to its own construction as well as in comparison to other constructs. All indicators should have the highest value with their own construct and the lowest value with other constructs or variables [27]. Appendix 2 demonstrates that the values of indicators are adequate with their own construct but less so with others, hence supporting the study. Standardized Root Mean Square Residual is defined as “the difference between the observed correlation and expected correlation of the variables or constructs of the model run.” This data’s value must be less than or equal to 0.10. The SRMR value of 0.0739 in this study demonstrates that the data satisfied the requirements. The partial least square regression model’s R square informs us how well it predicts the data set. This analysis highlights the value of internal model endogenous constructs. Its value should be bigger than 0.3. In this research, the value for Endogenous Purchase intention and Purchasing Behavior, respectively, are 0.645 and 0.735. This demonstrates that values met the threshold requirements [28]. Variance Inflation Factor (VIF) measures “how much the variance of the estimated regression coefficients is magnified as compared to when the predictor variables are not linearly related.” In short, this shows how much the variance is inflated or overestimated. Basically, this is a measure of Multicollinearity in data. There is a criterion value for VIF Values which suggests that if a value is less than 5 (VIF< 5) and a value near 1 shows that there is no link. Table 03 shows the VIF Values of all construct’s correlation are smaller than 5 and near 1, which suggests that no correlation exists between the linked hypothesis variables.

Table 03: Inner Model

The Variance Inflation Factor (VIF) of the outer model shows the correlation between elements. Value for this should be lesser than 5 of all the questions. In this research, all questions are in the range of (1.000 – 3.4239), showing that all questions are good enough and meet the criteria.

Figure 02: PLS-SEM Structural Model

Figure 2 shows the inner and outer models. The outer model represents factor loading values which should be greater than 0.7. The model shows the loading values of those elements which met the criteria, and other questions were excluded. Secondly, the inner model shows the impact of exogenous variables on endogenous variables. Starting from the impact of Effort Expectancy on Purchase Intention coefficient value is -0.004, which shows the impact of effort expectancy on purchase intention. This means that an increase in the Effort Expectancy of the customer by one unit will have a negative influence on Purchase Intention by -0.004 units; in other words, a 100% change in Effort Expectancy will bring a change in Purchase Intention by 0.4%. The impact of Performance Expectancy on Purchase Intention coefficient value is 0.037, which shows the positive impact of Performance Expectancy on purchase intention. The coefficient value shows that if we increase Performance Expectancy by one unit, it will have a positive influence on Purchase Intention by 0.037 units; in other words, a 100% change in Performance Expectancy leads to a 3.7% positive change in Purchase Intention of a customer. The impact of Perceived Risk on Purchase Intention coefficient value is -0.089, which shows the negative impact of Perceived Risk on Purchase Intention. The coefficient value shows that an increase in Perceived Risk by one unit will have a negative influence on Purchase Intention by -0.089 units, in other words, a 100% increase in Perceived risk leads to an 8.9% decrease in the purchase intention of a customer. The impact of Perceived Trust on Purchase Intention coefficient value is 0.799, which shows the positive highest impact of Perceived Trust on purchase intention. The coefficient value shows that if we increase Perceived Trust by one unit, it will have a strong positive influence on Purchase Intention by 0.799 units; in other words, if we increase Perceived trust by 100%, it will lead to 79.9% positive change in Purchase Intention of a customer. Now the impact of Purchase Intention on Purchase behavior coefficient value is 0.409, which shows the positive impact of Purchase Intention on Purchase behavior. The coefficient value shows that if we increase Purchase Intention by one unit. As a result, Purchase behavior will also increase by 0.409 units. Moreover, a by100% increase in Purchase Intention leads to a 40.9% increase in the purchase behavior of a customer.

4.1 PLS-SEM Path Analysis

The moderating impact of Social Influence Path Coefficient value of Moderating Effect of Social Influence is -0.023, low impact. The coefficient value shows that moderating impact of social influence exists but is just 2.3%, which means the moderating impact of negative social influence between Purchase Intention and Purchase Behaviour by 0.023 units. In other words, moderation has a negative impact, by 2.3%, on the relationship between Purchase Intention and Purchase behavior.

Figure 03: Bootstrapping Analysis

Now the coefficient of determination, R2, for Purchase Intension is 0.645, which shows all four exogenous variables (Effort Expectancy, Performance Expectancy, Perceived Risk, and Perceived Trust) explain 64.5% variance in Purchase Intension. R2 of Purchase Behaviour is 0.735, which shows all four latent variables, and also purchase intention explains 73.5% variance in Purchase Behaviour of a customer while making E-Purchase regards to service. The moderating impact of Social Influence Path Coefficient value of Moderating Effect of Social Influence is -0.023, low impact. The coefficient value shows that moderating impact of social influence exists but is just 2.3%, which means the moderating impact of negative social influence between Purchase Intention and Purchase Behaviour by 0.023 units. In other words, moderation has a negative impact, by 2.3%, on the relationship between Purchase Intention and Purchase behavior. Now the coefficient of determination, R2, for Purchase Intension is 0.645, which shows all four exogenous variables (Effort Expectancy, Performance Expectancy, Perceived Risk, and Perceived Trust) explain 64.5% variance in Purchase Intension. R2 of Purchase Behaviour is 0.735, which  Shows all four latent variables, and also, purchase intention explains 73.5% variance in the Purchase Behaviour of a customer while making E-Purchase regards to service.

Table 04: PLS-SEM Path Analysis

Now the coefficient of determination, R2, for Purchase Intension is 0.645, which shows all four exogenous variables (Effort Expectancy, Performance Expectancy, Perceived Risk, and Perceived Trust) explain 64.5% variance in Purchase Intension. R2 of Purchase Behaviour is 0.735, which shows all four latent variables, and also, purchase intention explains a 73.5% variance in the purchase. The behavior of a customer while making an E-Purchase regards service. The moderating impact of Social Influence Path Coefficient value of Moderating Effect of Social Influence is -0.023, low impact. The coefficient value shows that moderating impact of social influence exists but just2.3%, which means the moderating impact of negative social influence between Purchase Intention and Purchase Behaviour by 0.023 units. In other words, moderation has a negative impact, by 2.3%, on the relationship between Purchase Intention and Purchase behavior.  Now the coefficient of determination, R2, for Purchase Intension is 0.645, which shows all four exogenous variables (Effort Expectancy, Performance Expectancy, Perceived Risk, and Perceived Trust) explain 64.5% variance in Purchase Intension. R2 of Purchase Behaviour is 0.735, which shows all four latent variables, and also purchase intention explains 73.5% variance in purchase the behavior of a customer while making an E-Purchase regards service. Table 4 Path coefficient table shows path coefficient β values, which represent the impact of the exogenous variable on an endogenous variable, and also shows the T-Statistics and P values for testing the hypothesis. Accordingly, [29] T-Statistics value should be greater than 1.96, and P Value should be lesser than 0.05. These values criteria are the threshold for acceptance or rejection of the research hypothesis. Impact of the Social Influence Pathway that is Moderated The value of the moderating effect of social influence’s coefficient is -0.023, which indicates a low impact. The fact that the moderating impact of social influence exists is shown by the coefficient value, but it is only 2.3%, which indicates that the moderating impact of social has a negative influence on the relationship between purchase intention and purchase behavior by 0.023 units. To put it another way, the relationship between purchase intention and purchase behavior is negatively impacted by moderation to the tune of 2.3%. Now, the coefficient of determination, or R2, for Purchase Intention is 0.645, which demonstrates that all four exogenous variables (Effort Expectancy, Performance Expectancy, Perceived Risk, and Perceived Trust) explain 64.5% of the variance in Purchase Intention. The R2 value for Purchase Behaviour is 0.735, which indicates that all four latent variables, as well as Purchase Intention, explain 73.5% of the variance in Purchase Behaviour of a customer while they are making an E-Purchase in relation to a service. The PLS-SEM results are broken down by path coefficient values in Table 4, along with T-Statistics and P values. The value for the impact of effort expectation on purchase intention is -0.004, which shows a 0.04% impact; however, the value for T-Statistics is 0.0897, and the value for P is 0.9286; neither of these values meets the threshold criteria. Therefore, we will not accept H1 and will instead accept the alternative hypothesis, Ho. The value for the impact of Performance Expectancy on Purchase Intention is 0.037, which indicates that there is a significant 3.7% impact on Performance Expectancy. The T-Statistics value is 0.4589, and the P value is 0.6465; both of these values do not meet the threshold criteria of [30]. Therefore, we conclude that H2 is incorrect and instead accept the alternative hypothesis, which states that the customer’s performance expectations do not influence their intention to make a purchase. Value for the impact of Perceived Risk on Purchase Intention is -0.089, which shows an 8.9% negative or reversal impact of Perceived Risk, that if Perceived Risk increases, then Purchase intention will be decreased by 8.9%, as empirically supported in the article that Perceived risk has a negative influence on Customer Purchase Intention, and T-Statistics value is 2.204, and the P value is 0.02, both of which meet the threshold criteria of. Therefore, since there is an impact of perceived risk on customer purchase intention, we accept H3, but we do not accept the alternative hypothesis Ho, which states that there is no such impact. The t-Statistics value is 23.684, and the P value is 0.000, both of which meet the threshold criteria of [31]. The value for the impact of perceived trust on purchase intention is 0.799, which shows a positive and significant 79.9% impact of perceived trust on customer purchase intention. Therefore, we conclude that the null hypothesis Ho is more likely to be true than the alternative hypothesis H4, which states that there is an impact of perceived trust on purchase intention. The t-Statistics value is 10.456, and the P value is 0.000, both of which meet the threshold criteria good enough [32]. The value for the impact of Purchase Intention on Purchase Behaviour is 0.409, which shows a significant positive impact of 40.9% impact of Purchase Intention on Purchase Behaviour. Because of this, we conclude that the null hypothesis Ho is more likely to be true than the alternative hypothesis H5, which states that there is an impact of Purchase Intention on Purchase Behavior. The value of the moderating impact of social influence on the relationship between purchase intention and purchase behavior is -0.023, which indicates a negative lower moderating impact of just 2.3% on the relationship. Additionally, the value of T-Statistics is 1.96, and the value of P value is 0.03, both of which meet the threshold criteria of [33]. As a result, we will go with hypothesis 6 (H6) and dismiss the alternative hypothesis (Ho). Analysis of data from current Indian respondents shows that performance expectations and perceived trust have a beneficial effect on purchase intention. At the same time, the impact of effort expectation and social influence is unfavorable. These findings contrast sharply with those of a previous study in Finland that looked at both digital and non-digital commodities. That study demonstrates the larger effect of effort anticipation and social influence on purchasing behavior, while this study didn’t [34]. Social influence was added as a moderation variable, but after conducting the research, we came to know that most people in India don’t consider their peers, family members, etc., while purchasing services.

5. CONCLUSIONS

To sum up the whole discussion, the purchase intention of a prospective buyer to purchase a service through is positively affected by its performance expectancy. Similarly, perceived risks associated with a service in the mind of a customer while availing the opportunity to surf the Internet also influence the purchase intentions of the customer. In addition to this, results also indicate that perceived trust in a service in the mind of a customer plays a significant role in making up his mind customer to purchase that particular service being sold on the Internet. Furthermore, this is a proven fact that our actions are the reflections of the inner working of our minds. Our intentions constituted, whether at the conscious or sub-conscious level, are responsible for the behavior exhibited in our day-to-day life. This study further hints at the slight role of social influence on forming purchase intention to buy a service by using an internet facility. This is why the majority of people still prefer visiting a selling place to purchase a service physically still in our country, as the use of the Internet in our society is limited yet, and a huge number of customers buying services are unable to use the Internet frequently and fluently due to low rate of literacy, low availability of internet technology. However, the growing interest of people in utilizing internet technology will be helpful to a great extent to mold people towards purchasing services by resorting to visiting concerned service providers using the medium of internet technology. Analytically speaking, there are certain limitations in the study under observation. This study covers only a small portion of the targeted population. People belonging to every walk of life adopt online shopping services daily. Further, some particular services may be studied to discuss their pros and cons as there is a variety of services being sold online, and we cannot generalize our findings to the whole process of selling and buying services using online channels. Perusing the findings involved factors influencing the process of formation of purchase intentions of a buyer to purchase a service by visiting an online seller; this study points out that there are a lot of factors to be taken into consideration to make customers visit websites of organization in dealing in various services with feelings of trust, security, and intention to visit again. They should analyze the prevailing trends in society. In the future, organizations selling services online will make innovative strategies to draw the attention of purchasers of services by making them feel convenient, confident, and satisfied to cater to their needs and wants by adopting online business channels. This study has significance by discussing the influence of different factors responsible for the constitution of purchase intention leading to showing purchase behavior of a customer buying services on the internet.  Upward growth can be realized by addressing the issues observed in the instant study. This study has highlighted the need for further study to be conducted to trace the influences of factors creating purchasing intention, which leads to purchasing certain services while surfing relevant websites. This study has also pointed out that there is a wide scope for organizations providing services through online medium and their managers to understand the problems and requirements of customers of services visiting their websites. They may adopt effective strategies to look for their queries and methods to redress their grievances. Being vast and multifarious geographical, social, and cross-cultural levels, the online market of services not only provides opportunities for business but also for dynamic growth in a professional career for managers.

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