Keywords

Online Review Usefulness, Purchase Intention, Product Information, Price Perceptions, Product and Services Quality

Introduction

In the present scenario, online reviews play a vital role in consumers' decision-making process, and ultimately, they have a significant impact on products and services sales (Ghasemaghaei, 2016; Yaseen et al., 2020; Hervas-Drane, 2015). The producers do not originate the online customer reviews. However, the customers generally consider this user-generated content more incredible and impactful than those generated by the producers. A researcher Nielsen Company in 2013 conducted an extensive survey. Following his findings, it has been found that around 85% population collect information online by reviewing the other reviewer's or buyer's comments and reviews regarding any business so that they can make an appropriate purchasing decision (Filieri et al., 2018).

Usually, the information regarding the purchaser reviews is of two types provided on the online shopping sites, i.e., the first one is the review valence; it displays the information about the assessment and evaluation of the quality of products. The second one is the review usefulness in which the assessment of customer's product utility review is provided. In contrast to the previous framework, the persuasiveness of a reviewer's advice is heavily influenced by the customers' social ties to the reviewer (Filieri et al., 2018). Most of the studies suggest that the review valence should be first considered, but on the other hand, other studies suggest the greater use of review usefulness. For example, (Cheung et al., 2008) found that a review's perceived usefulness was the most important predictor of a consumer's chance of considering a review, implying that customers value review usefulness more than review valence when making purchasing decisions.

When considering these two factors that produce different conflicting predictions, if buyers are presented with both forms of information, one might ask how they will determine which is more diagnostic and, as a result, must be reviewed first and given more weight in their purchasing choice. Since Online Consumer Reviews (OCRs) have a great influence over the customer's buying decision, however, earlier studies suggest that online purchasing review on goods has shown significant impacts on the shoppers' good's assessment, buying choices, and buying behaviors (Filieri et al., 2018; Liu et al., 2020). Therefore, it has become challenging for the shoppers to orient themselves with the wealth of reviews available due to several reviews. Thus, it is crucial for all organizations that they will determine the information's determinants that they perceived from the online shopper's reviews (Filieri et al., 2018). While most of the shoppers are used to evaluate the product quality and services on online shopping websites that will then produce Word of Mouth (WOM) on the internet, according to the surveys conducted, less than 70% of shoppers depend on the reviewer's comment for their purchasing decision regarding the product quality and services offered. Around 85% of the shoppers point out that the reviewer's comment strongly impacts the buying behaviors (Shen et al., 2015). It has been found that most of the products are usually sold on the internet in a short period. It increases the customer's satisfaction and trust. Many shoppers are not only interested in the information regarding the products, but they are also highly focused on the reviewers' comments (Liu et al., 2020).

With the help of the Online Word-of-Mouth (EWOM) platforms, the customers can easily socially contact and interact with each other and get information regarding the quality of the product and services offered (Ahmad, 2019; Majali & Bohari, 2016). These platforms are expanding over the internet, more particularly for the products like music and books, i.e., from the Amazon store (amazon.com), for the electronic (shopping.com). Besides this, other services are also provided to the customer's services such as (Yelp.com; Tripadvisor.com; Hotels.com). Multiple reviews and comments are provided in considerable numbers, ranges, and completeness. Currently, the advancements in the online shopper's behaviors have made available complete information about the goods and services offered to the customers, unlike in the past in which they are not provided with much information. Several research and surveys have been conducted to evaluate the role of online reviews. Thus, most of the research has overlooked much of the aspects of online review. Most of the studies have focused highly on the underlying processes that drive consumers' perceptions of online reviews. Due to the abundance of online reviews and most of the problems are linked to them, so it is imperative to manage the uncertainty regarding the false comments so that the customers can easily rely upon and trust.

A report documented in Mintel (2015) has shown that in the US, about 81% of the shoppers, aged between 18 years to 34 years collect information from other individuals regarding the products before taking any buying decision. This research has predicted the influence of the online shopper's reviews as well as also provides predictions regarding the sales and revenues of different goods like books, restaurants, movies, and hotels (Filieri et al., 2018). Usually, most firms and organizations have enabled shoppers that they can leave a vote for each comment and review so that the other shoppers can easily assess the quality of the goods and services that are offered by the different organizations. Recently, most of the researchers have analyzed different online reviews data gathered from various e-retailers such as Amazon. Then applied the voting mechanism to estimate the reviews, receiving the most helpful votes (for instance, (Imtiaz, 2021; Ahmad & Laroche, 2015; Baek et al., 2012; Chua & Banerjee, 2016; Huang et al., 2015; Jabr & Zheng, 2014; Mudambi & Schuff, 2010; Pan & Zhang, 2011; Racherla & Friske, 2012; Yin et al., 2014).

From research, it has been found that the perceived usefulness is very significant in predicting the shopper's behavior and intention. Another researcher, Purnawirawan, et al., (2012), have documented the role of perceived usefulness. Thus, it can only be applicable when the reviews are usually perceived as useful, does positive vs. negative, and the increase vs. decreases review information about product purchase behavior. With the help of the Elaboration Likelihood Model (ELM), these results can be well- explained and evaluated. If the review usefulness is found to be high, so, the shoppers can employ the main route to process review contents, like they are highly attracted to the products that the other reviewers highly recommend. Thus, all the positive and negative reviews by the customers increase or decrease the purchases (Jaber et al., 2021; Jia & Liu, 2018).

If the review usefulness is low, so, the shoppers can take the peripheral route to process review contents, and as a result, situational factors, like the mood of the shopper and also the publishing website nature and, for example, the source credibility also needs to be determined their following a review's recommendations, even above and beyond the influence of review content (Jia & Liu, 2018). As such, this paper aims to explore determinants of online review usefulness and its influences on consumer's purchase decisions.

Literature Review

This section shows an overview of prior studies related to social experience in SC adoption, followed by a discussion of the theory used to develop the theoretical model using factors derived from the studies.

Online Review Usefulness and Purchase Decisions

Shoppers can get a large amount of information from online reviews. On the other hand, almost every offered goods or service on most websites are usually accompanied by many reviews (sometimes hundreds). An earlier study has shown several flaws and biases in the present research collection, aggregation, and listing techniques. Companies, for example, have incentives to provide promotional conversations or reviews to influence consumers' evaluations of items, according to Chevalier & Mayzlin (2006). Furthermore, because of the relative anonymity provided by the internet, businesses can (and do) disguise their marketing as customer suggestions.

Shoppers can get a large amount of information from online reviews. On the other hand, almost every offered goods or service on most websites are usually accompanied by many reviews (sometimes hundreds). An earlier study has shown several flaws and biases in the present research collection, aggregation, and listing techniques. Companies, for example, have incentives to provide promotional conversations or reviews to influence consumers' evaluations of items, according to Chevalier & Mayzlin (2006). Furthermore, because of the relative anonymity provided by the internet, businesses can (and do) disguise their marketing as customer suggestions.

Customer reviews on the internet have grown very common on a large number of online shopping sites. Customers use them to either search for goods that fit their tastes or gather information for offline transactions. Online customer reviews can usually be broken down into multiple dimensions. Researchers focused on various aspects have reported different implications on good selection and sales.' For example, in a study of valence, volume, and variance of online consumer evaluations, Kostyra (2015) discovered that volume and variance simply worked as moderators of valence's effect and had no direct influence on customers' purchase decision. Liu (2006) found that review volume, instead of review valence, enhances both aggregate and weekly box office revenue, emphasizing the number and valence of online movie reviews.

Duan, Gu & Whinston (2008) extended this conclusion by proposing a dynamic simultaneous equation system in which the two dimensions may be viewed as both a forerunner to and an outcome of retail. The individual's perception that employing new technology will increase or improve their performance is characterized as perceived usefulness. In the context of online commerce, usefulness refers to the degree to which customer believes that utilizing the internet as a medium will enhance their performance or efficiency, thereby enhancing their purchasing power. The result of the buying experience is linked to perceived usefulness (Almajali, 2016). The key advantages of internet shopping have frequently been stated as detailed information, accessibility, and speed, as well as the availability of inexpensive and convenient purchases (Cho & Sagynov, 2015). This leads to the following hypothesis:

H1 Online Review usefulness positively influences consumers to purchase decisions.

Product Information and Online Review Usefulness

The availability and speed of shopping may be extremely valuable qualities for seasoned online consumers who are unable to shop during typical business hours. The amount of information provided during shopping activities is one of the primary contrasts between traditional and online retailers. Customers are increasingly subjected to promotions presented by various media and salespeople in the offline world. Several businesses place a premium on individual sales over customer loyalty; that is, a salesman works extremely hard to sell a product, mainly in personal selling, emphasizing product qualities such as quality and ease of use. Customers can browse a considerable amount of data that gives detailed characteristics of goods and services in the digital context (Bilalet et al., 2020). Online customers can readily receive thorough reviews and information about desired things by using advanced systems, including a recommender system, a collaborative filtering system, and a feedback system. Consumers can also use complaint handling web platforms that are open to the public, such as Complaints.com and bbbonline.org, which provide customers' feedback, their opinions, comments, and product ratings. Thus, it has been found that the detailed product information which is available on online platforms significantly affects perceived usefulness (Cho & Sagynov, 2015).

H2 Product information positively influence online review usefulness.

Price Perception and Online Review Usefulness

One of the most crucial factors that affect Perceived Usefulness is price. The most interesting fact is that it possesses both repelling and attractive properties. The previous studies suggest that price is a complex stimulus and that individuals possess both negative and positive perceptions (Abdullah et al., 2017). Multiple studies have examined the relationship between price perception and shoppers' buying behavior, especially on online platforms. Most of the studies revealed that the perceived cost is highly associated with the shopper's attitude toward using new technology. Earlier research has also analyzed price sensitivity and price dispersion on the internet (Siering, 2018). It has been found that the shoppers in the online shopping environments are more concerned and sensitive about the price of the products since it will become easier for the shopper that they will make a comparison with the help of the price. It has been found that most of the shoppers in the online platforms are highly sensitive to price since they also have to the delivery charges and transaction fees that will negatively impact the shoppers' intention and buying behavior. Generally, the price of different shoppers is adversely affected by the price comparison sites and effective supply chain management. However, the expectations of the shoppers are usually decreased regarding online shopping. However, they are also based on how they perceive websites are helpful. Thus, it has been found that there is a strong association between the perceived usefulness and price in the online purchasing environment (Cho & Sagynov, 2015).

H3 Price perception positively influence online review usefulness.

Product and Service Quality and Online Review Usefulness

The shopper's review and judgment regarding the product excellence are termed as the perceived quality. Earlier studies analyzed perceived product and service quality as a continuum by developing the relationship between loyalty and satisfaction. It has been found that there is a difference between traditional services and online services. In the online shopping environments, perceived product and service quality is also based on reviews that affect attitudes regarding the goods and their offered service quality or buying decisions. Therefore, it has been found that perceived product and service quality greatly influences perceived usefulness in the online shopping environment (Cho & Sagynov, 2015).

H4 Product and service quality positively influence online review usefulness.

Theoretical Model and Measurement

The proposed theoretical model is presented in Figure 1, where each factor is derived from previous literature:

Methodology

This study gathered primary data by distributing 250 online questionnaires to Jordanian consumers, specifically those who had Amazon buying experiences. Slovin formula was used to determine the sample with an error term (e) of 0.05. The questions of each variable used Likert five-point scales where is scale 1 means Strongly Disagree and scale 5 means Strongly Agree. Data collection during four months, a total of 197 responses was collected. However, 16 questionnaires were excluded because they were outliers or not completed properly, which yielded a total of 181 usable questionnaires.

Data Analysis

After the quality assessment of the collected data, a multivariate statistical method namely PLS-SEM was applied to test the research framework, utilizing Smart PLS 3 Software to test the entire relationships in one go by correlating the latent variable and measurement items together (Hair, 2012). In structural equation modeling, the researcher needs first to assess the measurement model then proceed for structural model evaluation.

Assessment of the Measurement Models

The suggested model is a reflective-informative model which requires performing several verifications like, convergent validity test was carried out. In this investigation, items' loadings, Average Variance Extracted (AVE), and Composite Reliability (CR) was analyzed and the results are shown in Table 1. Based on the results, items' loadings were more than 0.7, which fulfilled the value recommended by Hair, et al., (2017). As for the AVE cut-off, the AVE should exceed 0.5 (Hair et al., 2009). In this study, the AVEs were in the range of 0.623 and 0.733, therefore were acceptable. Besides, the CR value ranged from 0.872 to 0.90, which is in accordance with the acceptable values suggested by Hair, et al., (2009).

Table 1 Results of the Measurement Model
	Items Loadings		Cronbach's Alpha	Composite Reliability	Average Variance Extracted (AVE)
Price Perception	PP1	0.817	0.806	0.873	0.633
	PP2	0.825
	PP3	0.809
	PP4	0.727
Product Information	PI1	0.842	0.826	0.885	0.658
	PI2	0.839
	PI3	0.81
	PI4	0.75
Product and Service Quality	PSQ1	0.886	0.833	0.891	0.733
	PSQ2	0.921
	PSQ3	0.753
Purchase Decision	PD1	0.844	0.851	0.9	0.693
	PD2	0.871
	PD3	0.847
	PD4	0.764
Online Review Usefulness	ORU1	0.803	0.804	0.872	0.629
	ORU2	0.815
	ORU3	0.793
	ORU4	0.761

After performing the test of convergent validity, the researcher has checked the discriminant validity. The test of discriminant validity was recommended by Fornell & Larcker (1981). Even though the Fornell–Larcker criterion was criticized in previous studies s (Henseler et al., 2015), however, this study used it to assess the discriminant validity and the results are shown in Table 2, all constructs show appropriate discriminant validity.

Table 2 Discriminant Validity Using Fornell and Lacker Criterion
Constructs	PP	PI	PSQ	PD	ORU
Price Perception	0.796
Product Information	0.527	0.811
Product & Service Quality	0.244	0.185	0.856
Purchase Decision	0.402	0.57	0.152	0.832
Online Review Usefulness	0.67	0.672	0.195	0.551	0.793

On the other hand, Henseler, et al., (2015) recommend a different approach to check the discriminant validity called the Heterotrait-Monotrait (HTMT) ratio of correlations. In addition, they also demonstrate the power of HTMT by means of a Monte Carlo simulation study. Given such a robust power technique, the current study also tested the discriminant validity by using the same technique. The rule of thumb of the HTMT test is if the HTMT value is greater than the HTMT value of 0.85 (Kline, 2011), or the HTMT value of 0.90 (Gold et al., 2001), then there is the problematic existence of discriminant validity. The result of the HTMT test is revealed in Table 3 and the values passed the required threshold (Kline, 2011; Gold et al., 2001). Hence, it indicates that the measurement model possesses sufficient discriminant validity.

Table 3 Heterotrait–Monotrait (HTMT) Test
Constructs	PP	PI	PSQ	PD	ORU
Price Perception
Product Information	0.641
Product and Service Quality	0.279	0.194
Purchase Decision	0.489	0.686	0.153
Review Usefulness	0.83	0.82	0.208	0.655

Assessment of Structural Model

As suggested by Hair, et al., (2011) that the coefficient of determination and the level of significance of the path coefficients (beta values) can be measured by the R². In this study, the engendered result was 0.303, which indicate the variance of consumers Purchasing Decision (PD) could be explained by online review usefulness which was also explained through Product Information (PI), Price Perception (PP), Product and Quality Service (PSQ) with 59 R². In further statistical, the calculation of the structural model and the bootstrapping analysis was performed. The finding shows in Table 4, that product information and price perception are positively associated with online review usefulness and statistically significant with β=0.440, β=0.436 respectively. Product and quality service were found to have an insignificant relationship with online review usefulness with β=0.008. Finally, the results show a strong association between online review usefulness and the consumers' purchase decisions with β=551 Thus, H1, H2, and H3 are supported. Table 5 shown the structure results followed by the validated model are shown in Figure 2.

Results and Discussion

Given the growing dispute among consumers towards the usefulness of online reviews and the rare research knowledge about determinants of consumers' perception, this study aims to investigate the determinants of online review usefulness and its influence on consumers' purchase decisions. The study provides several contributions to research and practice in the field of online commerce. Drawing on research variables, it contributes in the context of consumer's reliance on online reviews as a useful resource of information that enhances their purchase decisions. Moreover, the study provides empirical evidence of the determining role of product information and price perception in shaping consumers’ perceptions of online review usefulness. As a result of its connectedness, the causal model and the determinants confirmed a great illustrative power for understanding what motivates consumers’ perceptions of online review usefulness and consequent buying decisions.

An in further attention to the results 4 out of 5 proposed relationships significantly impact consumer perception of review usefulness and their buying decisions. For instance, it seems that there is a strong association between product information, price perception, and review usefulness, according to which buyers more likely rely on an online review, as it provides useful information and allow making prices comparison. These results are consistent with much previous research (Cho & Sagynov, 2015; Siering, 2018). Based on these findings, buyers consider the usefulness of review as a rich source of information that enhances their purchase decisions.

Product and service quality did not show any effect on review usefulness contrary to the hypothesized relationship, the possible explanation of this inconsistent finding may be found in the growing shopper interest in online reviews as such and, in specific, the improved awareness between consumers of fake reviews and businesses’ deceptive practices. Knowing that firms desire as many positive reviews as possible may make consumers feel doubtful in terms of product and services quality. As a result, they may perceive online reviews as less useful in terms of goods quality. However, the study result holds thought-provoking implications for practitioners, as the results confirm a positive impact of review usefulness on consumers’ purchase decisions and its determinants are important antecedents that significantly drawing consumers perceptions.

Conclusion

Review usefulness and its relationship with purchase decisions is a contemporary area of interest to researchers and practitioners. This study addressed the factors that draw consumers' perception of online reviews' usefulness. In this regard, several determinants are examined to explore what most features of online reviews are helpful and the impact of these reviews on consumers purchase decisions. After conducting data analysis, this study concluded that products information and price perception makes a strong belief among consumers toward the use of review, on the other hand, product and service quality do not influence consumers' perception in terms of usefulness. The outcome also indicates that consumers heavily rely on helpful reviews to improve their buying decisions. For future directions, several interesting research domains could be explored. For instance, technological features of online review, credibility, and fake practices such as review quantity and deceit content. By examine these factors, it can also improve the current understanding of the power of online review.

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Received: 06-Nov-2021, Manuscript No. asmj-21-7361; Editor assigned: 08-Nov-2021; PreQC No. asmj-21-7361(PQ); Reviewed: 23-Nov-2021, QC No. asmj-21-7361; Revised: 30-Nov-2021, Manuscript No. asmj-21-7361(R); Published: 06-Dec-2021