Trapping Fake Discounts as Drivers of Real Revenues and Their Impact on Consumer’s Behavior in India: A Case Study

This empirical study contributes towards identifying the effect of both fake and real discounts in the Indian marketing environment. A common but unsustainable practice in India is to increase the selling price and then offer a discount on the product. Increasing sales based on fake discount pricing strategy is a primary business development objective in India. The discounts, however, vary across store type and time and are based on product features. The selected databases were collected from the top five Indian e-commerce portals in terms of volume of sales, and from popular brick and motor outlets of tier 2 and tier 3 cities in India. The empirical results indicate that offers based on price in India had an impact of 2.8 times higher than the actual quality of the product. The outcomes suggest that marked price has a significant impact on consumer’s behavior. The results also indicate the existence of a strong correlation between trapping fake discounts and purchase by deceiving and persuading customers in India. Research is empirical in nature and respondents have been selected based on purposive sampling. The study is limited to tier 2 and 3 cities of India for 250 days, and the results are applicable to online and offline retail stores.


Introduction
Pricing is one of the most important parts of a company's marketing mix, as part of the marketing strategy. Pricing could be an authentic price, a lowered price showing discount, or the price which the commodity is sold at. A study of the literature reveals the effect of the varieties of prices on the behavior of the Indian customer [1]. This effect, and its extent, are of paramount importance to marketers and customers alike. The common practice in India is to increase the selling price and then offer a discount on the product. This article is based on malls in tier 2 and tier 3 cities and e-commerce portals in India to collect the data on discounts on offer. The data are on the sales of discount-offered goods and the effect on purchase decision taken by the customers in the malls. All the sellers practice discounts in some form or other. The justifications are varied for each one, some for managing their sales targets, some for inventory purposes to smoothen demand fluctuations, and the rest try to differentiate between customers' purchasing power. Lack of complete data on the part of the customers makes this dubious discount possible. This research is aimed at the influence of discounts on an ill-informed customer and his unfounded choices. This leads to marketers increasing the marked price to an imaginary high and then slashing the prices to induce customer purchase; 78% of the products surveyed in the malls across two cities in India had practiced this dubious and unsustainable pricing method [2]. The research gap identified and addressed in this paper is the degree of effect of discounts on the customer, as previous research is on the effects of the discount, but none have addressed to what extent it affects customer decision-making and harms them in the Indian market.
The study is limited to the purchases recorded in tier 2 and 3 cities in India, and was conducted over 250 days only. The malls were selected based on convenience sampling.
The main objectives of the study are: (1) To identify the degree of effect of discounts on customer decision-making.
(2) To identify how marked price is used as an indicator of quality.
The Indian customer associates the marked price with quality. This is the reason why dubious discounts are offered in the Indian market to increase sales. Is there a relation between the marked price and buying behavior? What if the customer is aware of the actual marked price? Do Indian customers use marked price as an indicator of quality? The aim of this article is to find answers to all of these questions.
The remainder of the research paper is organized as follows: In the Section 1, an introduction and other relevant aspects of the research topic are included. Section 2 presents a literature review. Section 3 includes the research methodology, and Section 4 includes the empirical analysis. The empirical results are presented in Section 5. Finally, we present a conclusion section, and the references come at the end of the research paper.

Literature Review
This study is associated with literature on pricing and retail across the globe. Objective pricing and its relation to quality in the minds of customers form the foundation of this article. This article focuses on fake discount; it reads into the impact of discounts on buying behavior. The outcomes point towards the regulations of dubious discounts and tricking of the customers. The literature review gives us a lot of study done on how buying behavior is related to price and its relation to product quality. On the other hand, aware consumers could be more likely to purchase deals on less-preferred brands compared with unaware consumers because of high discounts [28]. Although offering a high-price discount can increase consumers' perceptions of savings, it also has a negative effect on consumers' perceptions of product quality [29]. The bias that the higher the price, the higher the quality still influences the mentality of consumers in India. Moreover, fake discount is sometimes related to the piracy, imitation, counterfeiting, and forgery of famous brands. However, the lower cost is the most frequently cited motivation for buying brand counterfeit products [30]. Despite increasing efforts to improve mechanisms for the international enforcement of intellectual property rights, neither companies nor governments in industrialized countries appear able to curb the increasing supply and demand for counterfeits [31]. However, brand knowledge may act as a shield against credibility issues [32]. For the purpose of our empirical research study, dubious discounts have been defined as "no decrease from previous selling price, but a difference in the present selling price and fake introductory price" [2].
In the literature, a number of relevant aspects of consumer behavior have been discussed in the context of price discounts. Some researchers have argued that decrease in sales adversely affects low-priced and low-quality products [33]. Certain researchers have suggested that allowing the price discount to be increasing in the number of units increases willingness to pay sales value and retailers' revenue, and that a price discount that is uniformly distributed across units also has the potential to motivate consumers to buy more units of the product [34]. Other researchers have suggested that when price information is communicated using misleading practices, consumers develop lower levels of trustworthiness toward the source of information, as well as willingness to buy [35].A business-to-consumer commercial practice of misleading the average consumer is a very common phenomenon in India. Meanwhile policy makers should continue to assign significant time and resources to investigating concerns about misleading price comparison-based promotions [36].
Electronic copy available at: https://ssrn.com/abstract=3504469 Electronic copy available at: https://ssrn.com/abstract=3504469 Sustainability 2019, 11, 4637 5 of 20 Unfortunately, deceptiveness is a vague legal concept, including very different commercial practices [37]. However, including the individual characteristics of potential consumers has an influence on the buying process.
Understanding how people make online purchasing decisions is of growing importance [38]. The emergence and ever-increasing use of online commerce has triggered new behavioral dimensions and consumer possibilities to compare products or services, as their new status of online consumers are endless [39]. Unfortunately, the huge investment to grasp virtual consumers has induced online sellers to go no further towards the understanding of consumer behavior [40]. For instance, some researchers have concluded that although online shopping has provided a new international landscape to conduct business, the use of this medium and the absence of face-to-face interactions has presented the law with numerous challenges in terms of the scope of consumer protection [41].Some researchers investigated the impact of factors affecting consumers' trust in online product reviews and suggest that both argument quality and perceived similarity contribute to increased trust, but in varying degrees [42]. Moreover, in the case of payments that are automatically processed, such as online cashback, a benefit of separating a discount payment from the purchase event may arise out of positive spillover effects on future purchases rather than from imperfect redemption behavior [43].
Previous studies have revealed that the profit derived out of the relations between price and quality is based on the proportions of educated and uneducated customers [44]. The article is not based on informed customers or prices alone, as it finds a relationship between high price and its impact as an advertisement in itself [45,46]. On the other hand, dubious discounts do not increase the costs to the company like other variables [47]. The articles provide information on the nexus between price, quality, and value of it towards dubious discounts [48]. The forged price provides a fake discount, one which does not give any value but changes the impression of the customer. Discounts cloud the consumer's mind [18]. Customers use non-price characteristics of the products more [49,50] and take undue advantage of customers looking for value in pricing [51]. The impact of this has been researched on both dubious discounts and real ones, and this article identifies the popular yet unknown areas where offers of discount have actual and dubious parts. Attempts to inspect the effect of reduction in prices has resulted in ideas of reference vulnerability [52,53]. Such articles have noted multiple-mentioned situations that marketers rely on, such as marked prices that are advertised [54], other retailers' prices [55], outlet proposals [56], and dubious offers [57]. There is a recognizable range within which the discounts are effective [58]. There is an explanation based on the theory of allusion, supporting lost offer cost [47].
Pricing policy with multiple impacts on buying behavior is due to culture, despite the absence of foundation in established theory, like endings of pricing [53]. Consumers are continuously changing their attitudes, behavior, and approaches in domains of consumption, but price sensitivity is high regardless of their choice preference [59]. Consumers create a balance between expected costs and benefits, such as convenience, aesthetics, and price-but in today's market, the price point is such a powerful influence that environmental friendliness is something consumers may not consider when purchasing a product [60]. This article, however, uses customer knowledge on actual pricing. Articles have been written on the impact of dubious discounts and price comparisons on the welfare of customers [61]. The important point here is whether dubious discounts cheat, and if they do, how they adversely impact the customers [62,63]. This article tries to measure the impact of these dubious discounts on Indian customers.
The arguments to identify the effects of price drops have impacted the notions of dependence on reference [52]. The incomes from use of price to signal quality are highly dependent on informed and uniformed customers [44,51]. Research closely relates pricing as a silent salesman or advertisement [45,46]. The aspect of relation between prices, perceived quality, and the value it gives to dubious discounts are studied [48]. Numerous literatures exist on the effect of price reductions on behavior of consumers, beyond signaling quality [64]. Consumers do not search after getting discounts as they use non-price benefits [49,50] and discounted products as useful [51]. Research on dubious discounts and prices are Table 1. Products offered by online and offline stores surveyed during the time of survey.

Non-Discounted Discounted
Offline 6450 4554 Online 8400 6901 The customers were interviewed after the check out and bill payment at the counter, after observation of the purchase in the specified category where discounts were available. Customers were also questioned during the interview, after the purchase, on how they perceived the quality of the product. The interview method was selected as it was the most accurate method to capture the customers' feelings, perceptions, and opinions. It has high response rate among the Indian middle class, and allowed us detailed questioning of the respondents. Finally, customers were questioned during the interview, after the purchase, on how they perceive the quality of the product bought and what factors prompted the purchase.
The products that were considered for the research were the following: Footwear, mobiles, electronic devices, Wi-Fi devices, groceries, clothing, food and beverages, home and living products, watches, cosmetics, and bags. These are the broad categories into which all the products in Indian retail is divided into. All the products considered were from popular malls of Mangalore, Mysore, and Bangalore in India. The selected malls were the following: Forum and City Centre in Mangalore, Mantri, Phoenix in Bangalore, Forum and Mall of Mysore in Mysore, and the five popular e-commerce portals mentioned above. The databases on the above analysis cover a period of 250 days. The marked price, along with discounts, of the above products were noted. On the customer's side, the bill, date was noted respectively.
The type of research was empirical in nature and the type of sampling used for selection of respondents was purposive sampling. A total of 14,90,170 customers were observed during the process Electronic copy available at: https://ssrn.com/abstract=3504469 Electronic copy available at: https://ssrn.com/abstract=3504469 Sustainability 2019, 11, 4637 7 of 20 of the study. Practically, 14, 90, 170 is the number of footfalls in the selected malls over the course of the study in the selected categories. This is the total footfalls of all six malls selected for the study. Repeat purchase was also noted, and was been found to be a considerable number as shown in Figure 1. In online stores, 31% of sales was through return customers.
99% and standard error was set at 5. The customers were interviewed after the check out and bill payment at the counter, after observation of the purchase in the specified category where discounts were available. SPSS econometric software was used for data analysis and multiple regression analysis was conducted in order to analyze the data. The independent variable was "discount" and the dependent variable was "sales".

Data Analysis
There are multiple variants of the same merchandise from the same producer. Each type differs by its marked price. The marked cost is given when the product is initially given to market and it remains unchanged. Also, the price variations among the similar products differing in style may be because of some colors' deep connection with the Indian culture. Over 4000 styles of various merchandise were observed. Prices across online and offline stores in marked price with and without discounts are shown below in Table 2. The high-quality products are expensive when compared to lower quality products in both kinds of stores. There is a difference in marked prices, and online store on regular basis carry higher marked prices than offline stores. The same sellers' prices across online and offline stores are measured. Differences in pricing in online and offline are there, and occasionally offline store prices are more competitive than the online stores. The revenue is obtained by multiplying the number of goods sold to selling price, and multiplying the amount thus obtained by the discount percentage on offer. The amount thus obtained is converted into percent to obtain the revenue percentage to the company.  The study was conducted based on observation of respondents offline and by checking the number of goods sold online. The study was conducted over 250 working days in 2018. The researchers observed one category out of 11 on every day, for 6 h a day. The sample size was 750 based on the purposive sampling method. The sample size was calculated based on the population size of customers of these Brick and Mortar stores and e-commerce portals. Confidence level was 99% and standard error was set at 5. The customers were interviewed after the check out and bill payment at the counter, after observation of the purchase in the specified category where discounts were available. SPSS econometric software was used for data analysis and multiple regression analysis was conducted in order to analyze the data. The independent variable was "discount" and the dependent variable was "sales".

Data Analysis
There are multiple variants of the same merchandise from the same producer. Each type differs by its marked price. The marked cost is given when the product is initially given to market and it remains unchanged. Also, the price variations among the similar products differing in style may be because of some colors' deep connection with the Indian culture. Over 4000 styles of various merchandise were observed.
Prices across online and offline stores in marked price with and without discounts are shown below in Table 2. The high-quality products are expensive when compared to lower quality products in both kinds of stores. There is a difference in marked prices, and online store on regular basis carry higher marked prices than offline stores. The same sellers' prices across online and offline stores are measured.
Electronic copy available at: https://ssrn.com/abstract=3504469 Electronic copy available at: https://ssrn.com/abstract=3504469 Differences in pricing in online and offline are there, and occasionally offline store prices are more competitive than the online stores. The revenue is obtained by multiplying the number of goods sold to selling price, and multiplying the amount thus obtained by the discount percentage on offer. The amount thus obtained is converted into percent to obtain the revenue percentage to the company. Figure 2 shows the discounts offered by online and offline outlets during the study. The products sold offline are sold mostly at full prices, whereas lower quality high discount products are sold online. Most of the firms give discounts across all forms, irrespective of sales in that category.  Figure 2 shows the discounts offered by online and offline outlets during the study. The products sold offline are sold mostly at full prices, whereas lower quality high discount products are sold online. Most of the firms give discounts across all forms, irrespective of sales in that category. The discounts, however, vary across store type and time, and are based on product features. Discounts might be different at different stores and time periods. The discounting is strongly related to the products' newness in the market. The marked price and the discounted price are shown in Figure 3. Series1 shows average market price and Series2 shows the price after discount. The discounts, however, vary across store type and time, and are based on product features. Discounts might be different at different stores and time periods. The discounting is strongly related to the products' newness in the market. The marked price and the discounted price are shown in Figure 3. Series1 shows average market price and Series2 shows the price after discount.
The horizontal line shows the marked price of all products under observation. All sales over the life of a product is plotted, and none were sold at marked price. Over 89% of the goods were sold by the end of discounted period online. This Figure 89% was obtained by consolidating the data provided by all the outlets at the end of the discount period of sales of goods on discount. Customer attaches maximum worth to the newness of a product and remaining were to clear stocks.

Demand Management Process
The discrete choice model of miserly Indian middleclass has been presented here, and its parameters are given with respect to the data set. Measuring whether dubious discounts impact buying behavior, keeping all other features the same, is the objective of this section. The primary focus is comparison of original prices with dubious ones. The outcome shows that dubious discounts indeed have a high impact on purchases, and this mostly affects the ill-informed Indian customers. The discrete choice model is used to explain the role of discrete options, such as buying or not buying. This model is chosen to empirically test the choices of customers among a finite set of alternatives. Logistic regression is used for empirical analysis of discrete choice. The factors are: Discount, which is used by companies to quickly draw prospects into the online and offline stores.; dubious discounts, which artificially increase the marked price to give a sense of discount which is not real; store attributes, both online and offline, which practice both the form of discounts i.e., real or dubious; and consumer behavior, which guides us on how customers select the products.
The actual discounts help us identify the real benefit to the customer and the resulting changes in the sales graph. The dubious discounts give us the perceived benefit to the customer and the result of this marketing gimmick on the changes in the sales graph. The store attributes are also a factor which plays an important role in store selection. Consumer behavior give us insight into how customers purchase decisions vary across various prices and discounts.

Prototype
Item p, in shops, time period t given by attributes X pt, quality which cannot be defined Ep, marked price MPp, and selling price Ppst. The marked price may be: MPp= max s, t Ppst which is the real price and dubious one being MPp> max s, t Ppst. The difference between marked price and selling price is due to dubious and real discounts.
MPp − Ppst = (MPp − maxPpst ) + (maxPpst − Ppst) = f ake discount p + real discount pst (1) Electronic copy available at: https://ssrn.com/abstract=3504469 Electronic copy available at: https://ssrn.com/abstract=3504469 The outcome of a customer c, after buying product p, store s and time period t is given by the following equation: O cpst = α stPpst + Xptβ + yst f f ake discount p + ystr real discount pst + Ep + D (2) where α, β, γstf, γstr are the taste parameters of Indian customers, and D refers to changes in demand. The parameter varies across outlets to allow differences among Indian consumers, culturally and demographically. This is same as the earlier parameters that marked prices are shown as pointers of demand.
If D is considered to be iid Type-1 high point and changing the system of outcomes of share of market, mean utilities can be given as dpst: log(s pst) − log (s 0st) = α stPpst + X ptβ + y st f f ake discount p +y str real discount pst + Ep where s pst is market share, and s 0st is the share of outside good. When the customer visits an outlet but does not make a purchase, it is regarded as a purchase from outside. To enable us to identify this, a given time's footfall is measured, which helps estimate outside purchases.

Identification
The estimates of demand could be identified by the difference between discounted price and marked price. When the store is chosen by the customer and the same store has dubious discounts, then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table 3 as follows: Table 3. Randomly selected products.

Randomly Selected Products
Online Offline

4.2.Identification
The estimates of demand could be identified by the difference between discounted price and marked price. When the store is chosen by the customer and the same store has dubious discounts, then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table  3 as follows: The sign ₹ represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ₹53. The second column shows that online and offline products from the same seller are not significantly different.
The next three columns contain the outcomes of respective prices after discount on an average

4.2.Identification
The estimates of demand could be identified by the difference between discounted price and marked price. When the store is chosen by the customer and the same store has dubious discounts, then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table  3 as follows: Table 3. Randomly selected products.

Randomly selected products
Online The sign ₹ represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ₹53. The second column shows that online and offline products from the same seller are not significantly different.
The next three columns contain the outcomes of respective prices after discount on an average

4.2.Identification
The estimates of demand could be identified by the difference between discounted price and marked price. When the store is chosen by the customer and the same store has dubious discounts, then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table  3 as follows: Table 3. Randomly selected products.

Randomly selected products
Online The sign ₹ represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ₹53. The second column shows that online and offline products from the same seller are not significantly different.
The next three columns contain the outcomes of respective prices after discount on an average

4.2.Identification
The estimates of demand could be identified by the difference between discounted price and marked price. When the store is chosen by the customer and the same store has dubious discounts, then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table  3 as follows: Table 3. Randomly selected products.

Randomly selected products
Online The sign ₹ represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ₹53. The second column shows that online and offline products from the same seller are not significantly different.
The next three columns contain the outcomes of respective prices after discount on an average

4.2.Identification
The estimates of demand could be identified by the difference between discounted price and marked price. When the store is chosen by the customer and the same store has dubious discounts, then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table  3 as follows: Table 3. Randomly selected products.

Randomly selected products
Online The sign ₹ represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ₹53. The second column shows that online and offline products from the same seller are not significantly different.

4.2.Identification
The estimates of demand could be identified by the difference between discounted price and marked price. When the store is chosen by the customer and the same store has dubious discounts, then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table  3 as follows: Table 3. Randomly selected products.

Randomly selected products
Online The sign ₹ represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ₹53. The second column shows that online and offline products from the same seller are not significantly different.

4.2.Identification
The estimates of demand could be identified by the difference between discounted price and marked price. When the store is chosen by the customer and the same store has dubious discounts, then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table  3 as follows: Table 3. Randomly selected products.

Randomly selected products
Online The sign ₹ represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ₹53. The second column shows that online

4.2.Identification
The estimates of demand could be identified by the difference between discounted price and marked price. When the store is chosen by the customer and the same store has dubious discounts, then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table  3 as follows: Table 3. Randomly selected products.

Randomly selected products
Online The sign ₹ represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ₹53. The second column shows that online 4999 The sign 10 of 19 d be identified by the difference between discounted price and osen by the customer and the same store has dubious discounts, tes. The best situation to examine the pricing effect on purchase en without method to items, or where marked costs differ across prediction that marked price is not related to products which f predictions are misleading. For this to be wrong, the product able factor, and the marketer can estimate desirability of various . The first condition might be right, but the second requirement is erences in marked prices across categories are presented in Table   ble Table 3 gives variation of four randomly selected products in fline stores. Online products which have fake discounts and e analysis. Thus, online gives methods as to how to control the ed dubious offers, and the coefficient of this gives us fluctuation and online stores. s is provided with bigger discounts those are fake. However, not the amount of sales of a given piece of merchandise. It is hard to tionship among sales and marked prices of products, as prices ied features not specific to the products. To obtain impact of products from the same firm sold online and off line needs to be s among online and offline prices of a product from the same in relation to both. The outcomes of this are given in Table 4.
represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research Electronic copy available at: https://ssrn.com/abstract=3504469 Electronic copy available at: https://ssrn.com/abstract=3504469 uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ased on the prediction that marked price is not related to products which ese kinds of predictions are misleading. For this to be wrong, the product be a controllable factor, and the marketer can estimate desirability of various accordingly. The first condition might be right, but the second requirement is certain. Differences in marked prices across categories are presented in Table   Table 3 sents the Indian rupee currency symbol and INR is the International ardization currency code. Products sold online are subject to more dubious nes. Moreover, Table 3 gives variation of four randomly selected products in line and offline stores. Online products which have fake discounts and re used in the analysis. Thus, online gives methods as to how to control the ts are provided dubious offers, and the coefficient of this gives us fluctuation n both offline and online stores. ith low sales is provided with bigger discounts those are fake. However, not ting based on the amount of sales of a given piece of merchandise. It is hard to ity of a relationship among sales and marked prices of products, as prices mix of varied features not specific to the products. To obtain impact of nce between products from the same firm sold online and off line needs to be h differences among online and offline prices of a product from the same s regression in relation to both. The outcomes of this are given in Table 4. ed is for only one product. The outcomes in the first column give us the ct online being less than offline by ₹53. The second column shows that online m the same seller are not significantly different. umns contain the outcomes of respective prices after discount on an average are sold at ₹ 1451.3 under the the market price. The difference is considerable between low-and high-quality products of the same firm; this difference is at clear every product was sold, there exists no difference in strategies of online ch points that revenue and appeal of products in identification of demand in selection of discounts between online and offline products. 53. The second column shows that online and offline products from the same seller are not significantly different. tes of demand could be identified by the difference between discounted price and hen the store is chosen by the customer and the same store has dubious discounts, ses the sales estimates. The best situation to examine the pricing effect on purchase e marked cost is given without method to items, or where marked costs differ across riod, based on the prediction that marked price is not related to products which ved. These kinds of predictions are misleading. For this to be wrong, the product ld not be a controllable factor, and the marketer can estimate desirability of various t prices accordingly. The first condition might be right, but the second requirement is nd is uncertain. Differences in marked prices across categories are presented in Table   Table 3. Randomly selected products. ₹ represents the Indian rupee currency symbol and INR is the International r Standardization currency code. Products sold online are subject to more dubious ffline ones. Moreover, Table 3 gives variation of four randomly selected products in ver online and offline stores. Online products which have fake discounts and ata were used in the analysis. Thus, online gives methods as to how to control the products are provided dubious offers, and the coefficient of this gives us fluctuation exists in both offline and online stores. ndise with low sales is provided with bigger discounts those are fake. However, not iscounting based on the amount of sales of a given piece of merchandise. It is hard to vailability of a relationship among sales and marked prices of products, as prices d by a mix of varied features not specific to the products. To obtain impact of difference between products from the same firm sold online and off line needs to be establish differences among online and offline prices of a product from the same ch uses regression in relation to both. The outcomes of this are given in Table 4. recorded is for only one product. The outcomes in the first column give us the product online being less than offline by ₹53. The second column shows that online ucts from the same seller are not significantly different. ree columns contain the outcomes of respective prices after discount on an average oducts are sold at ₹ 1451.3 under the the market price. The difference is considerable scount between low-and high-quality products of the same firm; this difference is at e gap in clear every product was sold, there exists no difference in strategies of online s. Which points that revenue and appeal of products in identification of demand in trol for selection of discounts between online and offline products. 1451.3 under the the market price. The difference is considerable in prices after discount between low-and high-quality products of the same firm; this difference is at Rs 10 of 19 d by the difference between discounted price and stomer and the same store has dubious discounts, ituation to examine the pricing effect on purchase ethod to items, or where marked costs differ across at marked price is not related to products which are misleading. For this to be wrong, the product d the marketer can estimate desirability of various dition might be right, but the second requirement is rked prices across categories are presented in Table   y selected products.

Online
Offline currency symbol and INR is the International Products sold online are subject to more dubious es variation of four randomly selected products in nline products which have fake discounts and us, online gives methods as to how to control the ffers, and the coefficient of this gives us fluctuation tores. with bigger discounts those are fake. However, not f sales of a given piece of merchandise. It is hard to g sales and marked prices of products, as prices not specific to the products. To obtain impact of the same firm sold online and off line needs to be ne and offline prices of a product from the same both. The outcomes of this are given in Table 4. ct. The outcomes in the first column give us the ffline by ₹53. The second column shows that online significantly different. s of respective prices after discount on an average the the market price. The difference is considerable ality products of the same firm; this difference is at old, there exists no difference in strategies of online appeal of products in identification of demand in ween online and offline products.

Estimation and Results
The demand parameter is identified by regressing mean utility levels as shown in the Equation (3) on observables. The Indian market is shown here on outlet week. In every outlet week, the footfall number is the size of the market. The characteristics of the product Xpt consist of newness of the product and viability in the corresponding categories. The fixed effect of the outlet type and outlet week are also reported. Descriptive statistics of variables in the sample that is estimated is given in the Table 5, as follows:  Table 6 consists of the outcomes of demand estimation, and the regression estimates of mean utility are shown in the first column The variables that are exploratory are type of outlet and time period. Probability of purchase strongly correlates with both type of outlets, i.e., online or offline. The variable correlated to the price at which product is sold and age of the product. The following Table 6 presents the estimation of demand: Electronic copy available at: https://ssrn.com/abstract=3504469 Electronic copy available at: https://ssrn.com/abstract=3504469 Standard errors in brackets. *** p < 0.01, ** p < 0.05, * p < 0.1. Table 6 gives the interaction between actual and dubious discounts. This is used to control the effect of decrease of prices in actual and dubious discounts on buying behavior. The outcome is that actual discount has a bigger effect on online products than offline products, and dubious discounts impact buying behavior more than actual discounts. Table 6 has results from regulations in which the market price is taken as a regress at the place of discount variables. Comparison of co-efficient shows that a 10 of 19 entified by the difference between discounted price and the customer and the same store has dubious discounts, best situation to examine the pricing effect on purchase out method to items, or where marked costs differ across tion that marked price is not related to products which tions are misleading. For this to be wrong, the product tor, and the marketer can estimate desirability of various st condition might be right, but the second requirement is in marked prices across categories are presented in Table   ndomly  upee currency symbol and INR is the International code. Products sold online are subject to more dubious le 3 gives variation of four randomly selected products in ores. Online products which have fake discounts and sis. Thus, online gives methods as to how to control the ious offers, and the coefficient of this gives us fluctuation line stores. vided with bigger discounts those are fake. However, not ount of sales of a given piece of merchandise. It is hard to among sales and marked prices of products, as prices tures not specific to the products. To obtain impact of ts from the same firm sold online and off line needs to be g online and offline prices of a product from the same ion to both. The outcomes of this are given in Table 4. product. The outcomes in the first column give us the han offline by ₹53. The second column shows that online re not significantly different. tcomes of respective prices after discount on an average nder the the market price. The difference is considerable igh-quality products of the same firm; this difference is at was sold, there exists no difference in strategies of online 100 increase in an item's market price has the same impact on buying behavior as does an

4.2.Identification
The estimates of demand could be identified by the difference between discoun marked price. When the store is chosen by the customer and the same store has dubio then this increases the sales estimates. The best situation to examine the pricing effect decision is where marked cost is given without method to items, or where marked costs stores over a period, based on the prediction that marked price is not related to pro cannot be observed. These kinds of predictions are misleading. For this to be wrong desirability should not be a controllable factor, and the marketer can estimate desirabil products and set prices accordingly. The first condition might be right, but the second r not when demand is uncertain. Differences in marked prices across categories are prese 3 as follows: Table 3. Randomly selected products. The sign ₹ represents the Indian rupee currency symbol and INR is the Organization for Standardization currency code. Products sold online are subject to m discounts than offline ones. Moreover, Table 3 gives variation of four randomly selecte a price range over online and offline stores. Online products which have fake d corresponding data were used in the analysis. Thus, online gives methods as to how choice in which products are provided dubious offers, and the coefficient of this gives u in demand that exists in both offline and online stores.

Randomly selected products
The merchandise with low sales is provided with bigger discounts those are fake. H all firms allow discounting based on the amount of sales of a given piece of merchandis point out the availability of a relationship among sales and marked prices of produ could be affected by a mix of varied features not specific to the products. To obta relationship, the difference between products from the same firm sold online and off lin established. To establish differences among online and offline prices of a product fr firm, this research uses regression in relation to both. The outcomes of this are give Every outcome recorded is for only one product. The outcomes in the first column average price of product online being less than offline by ₹53. The second column show and offline products from the same seller are not significantly different.
The next three columns contain the outcomes of respective prices after discount o basis. Online products are sold at ₹ 1451.3 under the the market price. The difference is in prices after discount between low-and high-quality products of the same firm; this d Rs ₹ 60. The time gap in clear every product was sold, there exists no difference in strate 81 reduction of the price at which the product is sold. The change in market price does not add cost to the company, which has a bigger effect on customer, given estimates from a model of interruptions among market price and online product. The majority of the product sold has dubious discount and not actual product in bridle and motor shop have dubious discount, so the dubious online discounted product is served on dubious discount. The values point out that fake market prices have an effect on buying behavior that is 1.91 times more than the actual market prices. There are differences among online and offline products that impact the importance of marked prices. The outcomes throw light on dubious marked prices and the following discounts, which have impact on buying behavior that compete with real discounts. The outcomes give us reason for demand in the market as its effect on marketing decision. The analysis gives us understanding of how consumer awareness of the focus of the product neglects their effects.

Rate Prices and Consumer Heterogeneity
The lack of awareness on the consumers' part makes the market price a signal of quality [44]. The data on demand in the market, including market price and price after discount, give us the option to measure this signaling effect from price and quality correction, then their sensitivity to market price should be more than consumers who are aware.
The market space is varied from the laboratory research environment that uses the price-quality relationship. In this article, we focus on all products available for sale. Assumptions about betterment Electronic copy available at: https://ssrn.com/abstract=3504469 Electronic copy available at: https://ssrn.com/abstract=3504469 of quality in marginal cost with a company having high quality with lost cost on quality and hence the importance of keeping prices high. The present scenario has a high-quality product which is not expensive to the firm to produce, as demand is driven by aesthetics which do not increase production cost much. There are multiple reasons why the Indian middleclass consumer links price to quality. Firms with multiple products also try to push customers towards products that provide more value to the company, utilizing the customers' lack of awareness.
The inflated marked price list effects the structure of production cost, as quality is weakly connected to marginal cost. The willingness to show inflated market price reduces the need of pricing against marginal cost to increase income, while still relying on prices to convey quality. To identify the response of the Indian customer to dubious discounts and their relationship with awareness of the brands, this paper identifies relation among price-reducing variables, along with experience of users of the product, with the product. The experiences are day gap periods for which the consumer knows the store. This is based on the assumption that the longer the store has been operating in the mall (both tier 2 and tier 3), the more informed itis about pricing policies and dubious discounts. Another experience being the proximity between local outlets and the upperclass malls. The supposition about the variable is that the closer the local store, the higher the awareness will be of customers to note the variations in the assortments among channels and the existence of dubious discounts in the malls. The malls' fixed effects are taken for every regression, which are identified from the data from the outlets.
The following Table 7 presents the estimations on heterogeneity, such as: The identification has store-level interactions. The outlet time duration of existence is noted in column number 1. Exact store existence in years is available for all the outlets in the malls and e-commerce portals. Changes in the selected list of items point to the concerned store. Customers who are associated with malls and e-commerce sellers long-term experience less effect of dubious discounts on their buying behavior, but are sensitive to actual discounts. The second column shows the distance of malls from the regular outlets. The average distance of a mall from regular stand-alone stores is 35 m. There are some changes due to opening and closing of certain stores. The changes observed when a store opened reduced the distance to 20 m and 15 m when the store closeed among the distance variable of existing brick and mortar stores; 21 such instances were found. The increased distance between offline stores increased the response of the customers. The measurements are given in column 3, using two dummy values in the model. The measurements give proof that brand awareness reduces the sensitivity towards actual and dubious discounts. This leads us to believe that a dummy of consumer awareness would be misleading us about consumer beliefs about the prices. The dummy values may be correlated with other factors that may impact customers' reactions towards dubious discounts. The data obtained through observation is matched with the data obtained from experiments in which the sample was provided with data about actual marked prices.

Experimental Approach
The outcomes of experiments prepared identification of the impact of dubious discounts on purchase behavior. This supplements the observational data, as it gives us the independence to control the subjects of the samples' understanding of actual and fake discounts. The hypothesis that dubious discounts impact buyer behavior who is informed about the actual marked price is rejected. The relationship between reference price and buying behavior has been established by researchers before. The possibility of marked price controlled by marking the impacts for brands that are known and unknown was identified to even it [54]. The subjects of the experiment were shown footwear with the actual marked price. The footwear was select according to the category of product it belonged to in the observational data. The subjects were given information on marked price of the footwear, which was 10 of 19 e identified by the difference between discounted price and n by the customer and the same store has dubious discounts, . The best situation to examine the pricing effect on purchase without method to items, or where marked costs differ across ediction that marked price is not related to products which redictions are misleading. For this to be wrong, the product le factor, and the marketer can estimate desirability of various e first condition might be right, but the second requirement is nces in marked prices across categories are presented in Table   3. Randomly selected products. ian rupee currency symbol and INR is the International ency code. Products sold online are subject to more dubious , Table 3 gives variation of four randomly selected products in e stores. Online products which have fake discounts and analysis. Thus, online gives methods as to how to control the dubious offers, and the coefficient of this gives us fluctuation nd online stores. s provided with bigger discounts those are fake. However, not e amount of sales of a given piece of merchandise. It is hard to ship among sales and marked prices of products, as prices features not specific to the products. To obtain impact of oducts from the same firm sold online and off line needs to be mong online and offline prices of a product from the same relation to both. The outcomes of this are given in Table 4. one product. The outcomes in the first column give us the less than offline by ₹53. The second column shows that online ller are not significantly different. he outcomes of respective prices after discount on an average 51.3 under the the market price. The difference is considerable nd high-quality products of the same firm; this difference is at duct was sold, there exists no difference in strategies of online evenue and appeal of products in identification of demand in counts between online and offline products.
1200. Along with the actual price, a dubious discount was also given along with the actual marked price.
The subjects were given situations with actual and dubious discounts, ranging from Sustainability 2019, 11, x FOR PEER REVIEW 10 of

4.2.Identification
The estimates of demand could be identified by the difference between discounted price a marked price. When the store is chosen by the customer and the same store has dubious discoun then this increases the sales estimates. The best situation to examine the pricing effect on purcha decision is where marked cost is given without method to items, or where marked costs differ acro stores over a period, based on the prediction that marked price is not related to products whi cannot be observed. These kinds of predictions are misleading. For this to be wrong, the produ desirability should not be a controllable factor, and the marketer can estimate desirability of vario products and set prices accordingly. The first condition might be right, but the second requiremen not when demand is uncertain. Differences in marked prices across categories are presented in Tab 3 as follows: The sign ₹ represents the Indian rupee currency symbol and INR is the Internation Organization for Standardization currency code. Products sold online are subject to more dubio discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products a price range over online and offline stores. Online products which have fake discounts a corresponding data were used in the analysis. Thus, online gives methods as to how to control t choice in which products are provided dubious offers, and the coefficient of this gives us fluctuati in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, n all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard point out the availability of a relationship among sales and marked prices of products, as pric could be affected by a mix of varied features not specific to the products. To obtain impact relationship, the difference between products from the same firm sold online and off line needs to established. To establish differences among online and offline prices of a product from the sam firm, this research uses regression in relation to both. The outcomes of this are given in Table  Every outcome recorded is for only one product. The outcomes in the first column give us t average price of product online being less than offline by ₹53. The second column shows that onli and offline products from the same seller are not significantly different.
The next three columns contain the outcomes of respective prices after discount on an avera basis. Online products are sold at ₹ 1451.3 under the the market price. The difference is considerab in prices after discount between low-and high-quality products of the same firm; this difference is Rs ₹ 60. The time gap in clear every product was sold, there exists no difference in strategies of onli and offline goods. Which points that revenue and appeal of products in identification of demand

4.2.Identification
The estimates of demand could be identified by the difference between discounted marked price. When the store is chosen by the customer and the same store has dubious then this increases the sales estimates. The best situation to examine the pricing effect on decision is where marked cost is given without method to items, or where marked costs d stores over a period, based on the prediction that marked price is not related to produ cannot be observed. These kinds of predictions are misleading. For this to be wrong, th desirability should not be a controllable factor, and the marketer can estimate desirability products and set prices accordingly. The first condition might be right, but the second requ not when demand is uncertain. Differences in marked prices across categories are present 3 as follows: The sign ₹ represents the Indian rupee currency symbol and INR is the In Organization for Standardization currency code. Products sold online are subject to mo discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected p a price range over online and offline stores. Online products which have fake disc corresponding data were used in the analysis. Thus, online gives methods as to how to choice in which products are provided dubious offers, and the coefficient of this gives us in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. Ho all firms allow discounting based on the amount of sales of a given piece of merchandise. I point out the availability of a relationship among sales and marked prices of products could be affected by a mix of varied features not specific to the products. To obtain relationship, the difference between products from the same firm sold online and off line n established. To establish differences among online and offline prices of a product from firm, this research uses regression in relation to both. The outcomes of this are given Every outcome recorded is for only one product. The outcomes in the first column g average price of product online being less than offline by ₹53. The second column shows and offline products from the same seller are not significantly different.
The next three columns contain the outcomes of respective prices after discount on basis. Online products are sold at ₹ 1451.3 under the the market price. The difference is co in prices after discount between low-and high-quality products of the same firm; this diff Rs ₹ 60. The time gap in clear every product was sold, there exists no difference in strategie and offline goods. Which points that revenue and appeal of products in identification of 3000, with prices increasing by EER REVIEW 10 of 19 mand could be identified by the difference between discounted price and store is chosen by the customer and the same store has dubious discounts, ales estimates. The best situation to examine the pricing effect on purchase d cost is given without method to items, or where marked costs differ across sed on the prediction that marked price is not related to products which ese kinds of predictions are misleading. For this to be wrong, the product e a controllable factor, and the marketer can estimate desirability of various ccordingly. The first condition might be right, but the second requirement is ertain. Differences in marked prices across categories are presented in Table   Table 3. Randomly selected products. ents the Indian rupee currency symbol and INR is the International rdization currency code. Products sold online are subject to more dubious es. Moreover, Table 3 gives variation of four randomly selected products in ine and offline stores. Online products which have fake discounts and e used in the analysis. Thus, online gives methods as to how to control the s are provided dubious offers, and the coefficient of this gives us fluctuation both offline and online stores. ith low sales is provided with bigger discounts those are fake. However, not ng based on the amount of sales of a given piece of merchandise. It is hard to y of a relationship among sales and marked prices of products, as prices mix of varied features not specific to the products. To obtain impact of ce between products from the same firm sold online and off line needs to be differences among online and offline prices of a product from the same regression in relation to both. The outcomes of this are given in Table 4. d is for only one product. The outcomes in the first column give us the online being less than offline by ₹53. The second column shows that online the same seller are not significantly different. mns contain the outcomes of respective prices after discount on an average re sold at ₹ 1451.3 under the the market price. The difference is considerable etween low-and high-quality products of the same firm; this difference is at clear every product was sold, there exists no difference in strategies of online 200. Table 8 gives the total elements in the sample that belong to a particular group. A total of 750 observations were done. A five-point scale was used to rate their purchase likelihood. To identify the impact of every data on purchase likelihood, the following experiments were conducted. The outputs of regression are shown in Table 9. The actual marked price had an impact on buying behavior, but the displayed marked price had no effect. In the survey design stage, the actual price was displayed only in the first stages, but the displayed original price was presented during the testing of buying behavior.
The outcomes show that dubious discounts increase sales by manipulating customers. Individuals who were part of the observation sample ranked the discounts on specified criteria based on buying behavior.
The criteria were measured on a scale on which the rating was done. The criteria considered were: • Whether the sample considered the discount to be of value.

•
Whether the firm is trying to manipulate the buyer. • Subjects' and others' perceptions of the quality of the product.
The following Table 10 presents a detailed analysis of the variables and the results are relevant for a better understanding of consumer behavior in India: Standard errors in brackets. *** p < 0.001, ** p < 0.005, * p < 0.1. Note: Low R 2 is interpreted based on the large sample size chosen, along with good regression diagnostics. The impact of marked price as a variable has a high p-value, and thus has low R 2 score.
The model has a low R 2 because the actual marked price had an impact on the buying behavior of consumers, but on the other hand, the displayed marked price had no effect. The marked price that was affected did not have any significant effect on the research measurements. The subjects undergoing this research study doubted the authenticity of the firm more when the displayed price and actual price difference was high.
By regressing the measurements on prices, the above table records the outcomes. The findings show that higher marked prices made the customers think that the goods were of superior make. Subjects also believed that the discount had value: The higher the marked price, the truer the price is. The offer was considered 2.8 times more responsive to actual price than the quality of the product.
Electronic copy available at: https://ssrn.com/abstract=3504469 Electronic copy available at: https://ssrn.com/abstract=3504469 The marked price that was struck did not have any considerable effect on measurements. The subjects doubted the authenticity of the firm more when the displayed price and actual price difference was high.
The following Table 11 presents the buyer intention based on selected variables: By regressing buying behavior towards pricing and response ratings, the mediation analysis was done. The ratings of responses mediated the impact of pricing on buying behavior with the customer's perception of quality being more important than the offer itself. How others see quality did not have any considerable effect on the subject's own behavior. Ratings whether the firm is misleading its customers did not correlate with the buyer's behavior. Observations of multiple dimensions of consumers' responses are the same as the previous findings about prices and consumers' perceptions [53,66]. The findings show that demand spikes from dubious discounts observed were because of customers believing that fake marked price was the actual price. The parameter varied across outlets to allow differences among Indian consumers, culturally and demographically, and this is same as the earlier parameters that marked prices were shown as pointers of demand [67].
The marked price was directly proportional to the customer's perception of product quality and the value in the offer. This led to an increase in the percentage of sales. However, when the customer had the data on actual prices of the products, no effect on demand was shown, which stayed the same. However, the customer perceived the firm as misleading the customer. The customers need to understand that not all discounts are genuine, and they must check the marked prices with other sellers to arrive at the decision. If the marked price is increased, this gives a dubious discount. A well-informed customer will not fall into this trap, and can clearly understand a dubious discount from a real discount.

Conclusions
Price has been at the heart of promotions for almost all firms involved in marketing in offline and online platforms. But the impact of these modifications on buyer behavior is important to all the relevant stakeholders. Changes in the prices in the form of discounts represent a huge driver of Electronic copy available at: https://ssrn.com/abstract=3504469 Electronic copy available at: https://ssrn.com/abstract=3504469 demand in the online and offline markets, which also involves low cost on the part of the marketers. Customers try to assess whether the prices inform or cheat them. The outcomes prove that marked price has a significant impact on buying behavior. The outcomes indicate that customers perceive stores over a period, based on the prediction that marked price is not related to pro cannot be observed. These kinds of predictions are misleading. For this to be wrong desirability should not be a controllable factor, and the marketer can estimate desirabil products and set prices accordingly. The first condition might be right, but the second r not when demand is uncertain. Differences in marked prices across categories are prese 3 as follows: The sign ₹ represents the Indian rupee currency symbol and INR is the Organization for Standardization currency code. Products sold online are subject to m discounts than offline ones. Moreover, Table 3 gives variation of four randomly selecte a price range over online and offline stores. Online products which have fake d corresponding data were used in the analysis. Thus, online gives methods as to how choice in which products are provided dubious offers, and the coefficient of this gives u in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. H all firms allow discounting based on the amount of sales of a given piece of merchandis point out the availability of a relationship among sales and marked prices of produ could be affected by a mix of varied features not specific to the products. To obta relationship, the difference between products from the same firm sold online and off lin established. To establish differences among online and offline prices of a product fr firm, this research uses regression in relation to both. The outcomes of this are give Every outcome recorded is for only one product. The outcomes in the first column average price of product online being less than offline by ₹53. The second column show and offline products from the same seller are not significantly different.
The next three columns contain the outcomes of respective prices after discount o basis. Online products are sold at ₹ 1451.3 under the the market price. The difference is in prices after discount between low-and high-quality products of the same firm; this d Rs ₹ 60. The time gap in clear every product was sold, there exists no difference in strate and offline goods. Which points that revenue and appeal of products in identification efficient can control for selection of discounts between online and offline products. iven without method to items, or where marked costs differ across e prediction that marked price is not related to products which of predictions are misleading. For this to be wrong, the product llable factor, and the marketer can estimate desirability of various y. The first condition might be right, but the second requirement is fferences in marked prices across categories are presented in Table   able  Indian rupee currency symbol and INR is the International currency code. Products sold online are subject to more dubious ver, Table 3 gives variation of four randomly selected products in ffline stores. Online products which have fake discounts and the analysis. Thus, online gives methods as to how to control the ided dubious offers, and the coefficient of this gives us fluctuation ne and online stores. les is provided with bigger discounts those are fake. However, not n the amount of sales of a given piece of merchandise. It is hard to ationship among sales and marked prices of products, as prices aried features not specific to the products. To obtain impact of n products from the same firm sold online and off line needs to be ces among online and offline prices of a product from the same n in relation to both. The outcomes of this are given in Table 4. nly one product. The outcomes in the first column give us the ing less than offline by ₹53. The second column shows that online e seller are not significantly different. in the outcomes of respective prices after discount on an average ₹ 1451.3 under the the market price. The difference is considerable w-and high-quality products of the same firm; this difference is at product was sold, there exists no difference in strategies of online hat revenue and appeal of products in identification of demand in f discounts between online and offline products. .0*** 247.6*** 105.1*** 93.5*** 98.2*** 100 marked price. In other words, the effect of discounts on sales has been identified as an increase of ates. The best situation to examine the pricing effect on purchase given without method to items, or where marked costs differ across the prediction that marked price is not related to products which s of predictions are misleading. For this to be wrong, the product ollable factor, and the marketer can estimate desirability of various ly. The first condition might be right, but the second requirement is ifferences in marked prices across categories are presented in Table   Table 3. Randomly selected products. Indian rupee currency symbol and INR is the International n currency code. Products sold online are subject to more dubious eover, Table 3 gives variation of four randomly selected products in offline stores. Online products which have fake discounts and the analysis. Thus, online gives methods as to how to control the vided dubious offers, and the coefficient of this gives us fluctuation line and online stores. ales is provided with bigger discounts those are fake. However, not on the amount of sales of a given piece of merchandise. It is hard to elationship among sales and marked prices of products, as prices varied features not specific to the products. To obtain impact of een products from the same firm sold online and off line needs to be nces among online and offline prices of a product from the same on in relation to both. The outcomes of this are given in Table 4. only one product. The outcomes in the first column give us the eing less than offline by ₹53. The second column shows that online e seller are not significantly different. tain the outcomes of respective prices after discount on an average t ₹ 1451.3 under the the market price. The difference is considerable low-and high-quality products of the same firm; this difference is at ry product was sold, there exists no difference in strategies of online that revenue and appeal of products in identification of demand in of discounts between online and offline products. 100 on the listed price has an impact equal to a discount of then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table  3 as follows: The sign ₹ represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ₹53. The second column shows that online and offline products from the same seller are not significantly different.
The next three columns contain the outcomes of respective prices after discount on an average basis. Online products are sold at ₹ 1451.3 under the the market price. The difference is considerable in prices after discount between low-and high-quality products of the same firm; this difference is at Rs ₹ 60. The time gap in clear every product was sold, there exists no difference in strategies of online and offline goods. Which points that revenue and appeal of products in identification of demand in efficient can control for selection of discounts between online and offline products.  81. However, the impact drastically reduces when the customer is aware of the original list price. The outcomes prove that there is a strong correlation between misleading discounts and purchase by deceiving customers about the actual prices.
This research found that offers based on prices in India had an impact of 2.8 times higher than the actual quality of the product. This study found that Indian consumers use actual price as an indicator of quality. However, a third of the sampled consumers doubted the offer when the discount was near or more than half the actual price. The outcome is that customers perceive

4.2.Identification
The estimates of demand could be identified by the difference between discounted price and marked price. When the store is chosen by the customer and the same store has dubious discounts, then this increases the sales estimates. The best situation to examine the pricing effect on purchase decision is where marked cost is given without method to items, or where marked costs differ across stores over a period, based on the prediction that marked price is not related to products which cannot be observed. These kinds of predictions are misleading. For this to be wrong, the product desirability should not be a controllable factor, and the marketer can estimate desirability of various products and set prices accordingly. The first condition might be right, but the second requirement is not when demand is uncertain. Differences in marked prices across categories are presented in Table  3 as follows: The sign ₹ represents the Indian rupee currency symbol and INR is the International Organization for Standardization currency code. Products sold online are subject to more dubious discounts than offline ones. Moreover, Table 3 gives variation of four randomly selected products in a price range over online and offline stores. Online products which have fake discounts and corresponding data were used in the analysis. Thus, online gives methods as to how to control the choice in which products are provided dubious offers, and the coefficient of this gives us fluctuation in demand that exists in both offline and online stores.
The merchandise with low sales is provided with bigger discounts those are fake. However, not all firms allow discounting based on the amount of sales of a given piece of merchandise. It is hard to point out the availability of a relationship among sales and marked prices of products, as prices could be affected by a mix of varied features not specific to the products. To obtain impact of relationship, the difference between products from the same firm sold online and off line needs to be established. To establish differences among online and offline prices of a product from the same firm, this research uses regression in relation to both. The outcomes of this are given in Table 4. Every outcome recorded is for only one product. The outcomes in the first column give us the average price of product online being less than offline by ₹53. The second column shows that online and offline products from the same seller are not significantly different.
The next three columns contain the outcomes of respective prices after discount on an average basis. Online products are sold at ₹ 1451.3 under the the market price. The difference is considerable in prices after discount between low-and high-quality products of the same firm; this difference is at Rs ₹ 60. The time gap in clear every product was sold, there exists no difference in strategies of online and offline goods. Which points that revenue and appeal of products in identification of demand in efficient can control for selection of discounts between online and offline products.   d by the difference between discounted price and stomer and the same store has dubious discounts, ituation to examine the pricing effect on purchase ethod to items, or where marked costs differ across at marked price is not related to products which are misleading. For this to be wrong, the product d the marketer can estimate desirability of various dition might be right, but the second requirement is rked prices across categories are presented in Table   y  currency symbol and INR is the International Products sold online are subject to more dubious es variation of four randomly selected products in nline products which have fake discounts and us, online gives methods as to how to control the ffers, and the coefficient of this gives us fluctuation tores. with bigger discounts those are fake. However, not f sales of a given piece of merchandise. It is hard to g sales and marked prices of products, as prices not specific to the products. To obtain impact of the same firm sold online and off line needs to be ne and offline prices of a product from the same both. The outcomes of this are given in Table 4. ct. The outcomes in the first column give us the ffline by ₹53. The second column shows that online significantly different. s of respective prices after discount on an average the the market price. The difference is considerable ality products of the same firm; this difference is at old, there exists no difference in strategies of online appeal of products in identification of demand in ween online and offline products. 100 marked price. The demand generated by dubious discounts was through manipulation of the customers who believed the fake marked price to be real. This study found that when actual marked price was revealed, the displayed marked price effect was zero on the customers. The outcomes show that, when marked prices is same, an increased real marked price gives an increased purchase intent.
The moderating factor here is the time period at which the stores are open, as well as the distance between brick and mortar stores-which shows that customer awareness about actual price reduces the effect of discounts, both offline and online, on buying behavior. The lab experiments also prove the same. Customers believe high price to be indicator of a quality. The lab experiments have been conducted with the limitations. This article, however, opens doors for further research on the challenges faced by firms in the area of dubious discounts. However, the discounts offered in the Indian market point to the fact that it is profitable to the firms. The practice of discounts has a repetitive aspect to it [68]. Lawmakers must identify these practices and regulate fake discounts. Prior studies have suggested that the anchoring bias for regulation appears to be a robust tool for determining whether consumers are systematically deceived [69]. Changing the system of outcomes of share of market is something that has also been used in previous studies [70]. Basically, all the previous articles say that dubious discounts are a way to cheat customers who lack knowledge about prices. There exists a strong relationship among quality and cost of production which sets the marked price, which in turn indicates quality [2]. Quality could be a variable factor, as in the case of production companies, which may be controlled through production process by companies. These companies use dubious discounts as a method to overcome uneven data about quality of products in the market. Further, many industries have varied qualities, and such an idea will fit those industries well.
This empirical study contributes towards identifying the effect of both fake and real discounts in the Indian marketing environment. This article has been inspired by numerous research papers on discounts, and has identified the repetitive nature of discounts by sellers. This article shows that discounts have an impact only when perceived as genuine, and sellers need to focus on actual discount to drive sales. Also, the sellers can use the effect to which marked price is used as an indicator of quality by Indian customers. This article leaves more room for research for academics as to how time and place also have an impact on the offers, as India has many festivals and holidays.