{"id":55,"date":"2023-12-05T15:19:49","date_gmt":"2023-12-05T15:19:49","guid":{"rendered":"https:\/\/commerce-management.kmics.ac.in\/?page_id=55"},"modified":"2024-01-07T06:55:20","modified_gmt":"2024-01-07T06:55:20","slug":"madhuri-thakur-jan-5-2023","status":"publish","type":"page","link":"https:\/\/commerce-management.kmics.ac.in\/index.php\/madhuri-thakur-jan-5-2023\/","title":{"rendered":"Madhuri Thakur, Jan 5, 2023"},"content":{"rendered":"\n
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An Analytical Study on Consumer Behavior Pertaining to e-Markets<\/h1>\n\n\n\n

Madhuri Thakur
Telangana Mahila Vishwa Vidhyalayam, Koti, Hyderabad, Telangana.<\/p>\n\n\n\n

ABSTRACT<\/h2>\n\n\n\n

As the world is moving towards digitalization, the e-commerce sector has experienced a big jump in a short tenure. This has increased the number of e-commerce companies competing with each other to capture the market share. The rapid increase in the number of online buyers, hike in mobile penetration including in tier 2 & tier 3, secure payment gateways, low entry & exit barriers, tremendous increase in demand and enhanced logistics are the some of the major contributors proliferating the online market. In the coming years, it is expected that the e-commerce sector will flourish to the next level.
Purpose: To study the consumer behavior of men and women towards the digital markets.
Design: The primary data is collected from 131 customers from Telangana State using Google Forms. The questionnaire is divided into two parts. The first part contains data collection on demographic profiles. The second part collects specific information about the Fintech services availed by the customers. For general information nominal and ordinal measurements are used. A few variables are measured through dichotomous questions and the remaining variables are measured through a 5-point Likert scale. The collected data is processed and frequencies cross-tabulation and Chi-square tests are used to know if there is any significant difference in the behavior of male and female consumers over digital markets.
Findings: Four hypotheses were constructed to test if there is any significant difference in the behavior of men and women over digital markets. The data was collected from the respondents from 4 attributes i.e., E-Enjoyment, E-Distrust, E-Offers and E-Price .Each attribute was subdivided into five questions. All the four hypotheses have been rejected implying that there is significant difference in the opinions and behavior of males and females on digital markets.<\/p>\n\n\n\n

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Received<\/strong>: Oct 3, 2022 | Accepted<\/strong>: Dec 10, 2022 | Published<\/strong>: Jan 5, 2023<\/p>\n\n\n\n

Keywords<\/strong>: Digital markets, Consumer Behavior, e-Enjoyment, e-Distrust, e-Offers and e-Price.<\/p>\n\n\n\n

Citation<\/strong>: Madhuri Thakur (2023) An Analytical Study on Consumer Behavior Pertaining to e-Markets. KMICS Journal of Commerce and Management, 1(1). https:\/\/doi.org\/10.62011\/kmicsjcm.2023.1.1.6<\/p>\n\n\n\n

Correspondence<\/strong>: madhurithakur03@gmail.com<\/p>\n\n\n\n

Competing interests<\/strong>: The author has declared that no competing interests exist.<\/p>\n\n\n\n

Copyright<\/strong>: \u00a9 2023 Madhuri Thakur. This is an open-access article. The use, distribution, and reproduction of this article in any medium is unrestricted, provided the original author and source are cited.<\/p>\n\n\n\n

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INTRODUCTION<\/h2>\n\n\n\n

As the world is moving towards digitalization, the e-commerce sector has experienced a big jump in a short tenure. This has increased the number of e-commerce companies competing with each other to capture the market share. The Rapid increase in the number of online buyers, hike in mobile penetration including in tier 2 & tier 3, secure payment gateways, low entry & exit barriers, tremendous increase in demand and enhanced logistics are some of the major contributors proliferating the online market. In the coming years, it is expected that the e-commerce sector will flourish to the next level. The online apparel industry is expected to increase at an annual growth rate of 10.6% until 2022. According to the statistical report, the compound annual growth rate for retail e-commerce sales in India is expected to reach 13.1% by 2024. The current penetration of the online industry stands at 28% in India. Global online retail sales are expected to reach 6,388 billion US dollars by 2024. Cheap labor availability, low prices of raw material, and supportive government policies are some of the factors uplifting the Indian retail industry as a whole.<\/p>\n\n\n\n

Factors Influencing Consumer Buying Behaviour<\/strong><\/p>\n\n\n\n

With a sudden spike in online shoppers, there is a need to study the needs and buying behavior of online buyers. Many models and theories were identified in the literature on online consumer behavior. \u201cThe online consumer has the double identity, a shopper and a computer user.  The intention to return or re-purchase strongly depends on e-enjoyment and perceived site usability indicated. Nine parameters that are relevant for identifying an online consumer\u2019s purchase behavior are: quality of personalization, shopping enjoyment, persuasion, price, promotion, service quality, store brand sensitivity, shopping enjoyment, intention to purchase and innovativeness. Pandey and Chawla created a six-factor scale: e-enjoyment, e-distrust, e-self inefficacy, e-logistic concerns, e-negative beliefs and e-offers, in order to study an Indian online consumer.<\/p>\n\n\n\n

\u2022 E-Enjoyment<\/strong> \u2013 It comprises all the benefits and ease that e-shopping provides to the consumer. It considers pleasure, arousal & attitude, or approach as the indicators of online shopping satisfaction. Similarly, some researchers consider time-saving & cost-saving while some other researchers consider content, exactness, layout, user-friendliness, and timeliness as the pointers of online shopping satisfaction.<\/p>\n\n\n\n

\u2022 E-distrust<\/strong> \u2013It is defined as the trust associated with e-shopping. Consumers are skeptical about e-shopping and therefore avoid sharing their personal details. Pradas reported that the most commonly discussed barrier to the budding consumers involved in online shopping is the need to tangibly see the item for consumption before making a purchase followed by fears pertaining to data security, the safety of e-commerce, and unreliability of payment structures.<\/p>\n\n\n\n

\u2022 E- Offers<\/strong>– The offers and the discounts offered to the consumers have a positive impact on the buying behavior of the consumers.  Cash or price discount is one of the most commonly practiced techniques of sales promotion. This technique plays an imperative role in the purchase behavior of a consumer.<\/p>\n\n\n\n

\u2022 E-Price<\/strong> \u2013 The price of the product affects the buying behavior of the consumers. As far as consumer choice is concerned, price plays a crucial role then repeatedly accredited to it and is also viewed as a seminal factor.<\/p>\n\n\n\n

Theoretical definition of the parameters and item creation for Consumer Buying behavior<\/strong><\/p>\n\n\n\n

Parameter<\/td>Definition<\/td>Items<\/td><\/tr>
E-Enjoyment<\/td>It comprises of all the benefits & ease that e-shopping provides to the consumer<\/td>\u2022 Item 1: Fun of using website \u2022 Item 2: Ease of use \u2022 Item 3: Online variety \u2022 Item 4: Product Quality & Info \u2022 Item 5: website design<\/td><\/tr>
E-Distrust<\/td>It is defined as the trust associated in e-shopping. Consumers are skeptical about e-shopping and avoid sharing their personal details.<\/td>\u2022 Item 1: Security Concerns \u2022 Item 2: E-Privacy \u2022 Item 3: Internet Distrust \u2022 Item 4: E- Shopping Risk \u2022 Item 5: Return Policy    <\/td><\/tr>
E-Offers<\/td>The offers provided to online consumers in context of variety coverage and discounts, which elicits a positive consumer buying behavior.<\/td>\u2022 Item 1: Special Offers \u2022 Item 2: Online Vs Offline Offers \u2022 Item 3: Brand offer comparison \u2022 Item 4: Banner Ads \u2022 Item 5: Online Product Availability<\/td><\/tr>
E-Price<\/td>It is the amount that the consumer spent in purchasing the product online.<\/td>\u2022 Item 1: High Price Risk \u2022 Item 2: Price vs Quality \u2022 Item 3: Impulse Buying \u2022 Item 4: Value for Money \u2022 Item 5: Price vs Quantity<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Review of Literature<\/h2>\n\n\n\n
S.No.<\/strong><\/td>Author & Year<\/strong><\/td>Focus<\/strong><\/td>Findings<\/strong><\/td><\/tr>
1.<\/strong><\/td>Rai, 2017<\/td>Consumer Buying decision<\/td>For the progress of digital channels in the consumer buying decision, shopping via digital media is reflected to be an optimistic indication as consumer seems to be satisfied with the purchase they have made.<\/td><\/tr>
2.<\/strong><\/td>Lodhi and Shoaib, 2017<\/td>Consumer buying behavior<\/td> Internet security has a substantial bond with consumer virtual buying behavior E-Marketing is the future of Marketing<\/td><\/tr>
3.<\/strong><\/td>Sharma, 2017<\/td>Digital Marketing<\/td>Well-read Individuals are aware of the electronic channel and choose to buy from there.<\/td><\/tr>
4.<\/strong><\/td>Lodhi and Shoaib, 2017<\/td>E-marketing<\/td>All the independent E-marketing factors are evidently related to e-marketing; such as- print media marketing, Web marketing, Goods & services marketing FMCG marketing, Electronic media marketing, E- marketing, Global marketing.<\/td><\/tr>
5.<\/strong><\/td>Afzal, 2015<\/td>consumer buying behavior<\/td>Factors influencing consumer buying behavior are- design of the product Quality of the product Content of advertisement Loyalty of consumer Past purchase experience<\/td><\/tr>
6.<\/strong><\/td>Park & Lennon, 2009<\/td>Brand image and apparel industry<\/td>A robust brand name has an encouraging influence on the virtual store image and observed importance, which results into buying intent in the virtual purchase of apparel.<\/td><\/tr>
7.<\/strong><\/td>Kim, 2015<\/td>Online shopping<\/td>To be competitive, it is vital for the web-based shopping industry to understand and identify as to what customers take note of and what make them repeat their purchase.<\/td><\/tr>
8.<\/strong><\/td>Rhee & Johnson, 2012<\/td>Apparel industry and brand image<\/td>In apparel articles figurative feature is vital as it represents self- image.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

OBJECTIVES OF THE STUDY<\/h2>\n\n\n\n
    \n
  1. To study the behavior of male and female consumers in Digital markets.
    1. To study the behavior of male and female consumers on e-Enjoyment while using digital markets.<\/li><\/ol>
      1. To study the behavior of male and female consumers in e-Distrust on digital markets.<\/li><\/ol>
        1. To study the behavior of male and female consumers on e-Offers in digital markets.<\/li><\/ol>\n
            \n
          1. To study the behavior of male and female consumers on e-Price offered on digital markets.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n\n\n\n

            RESEARCH METHODOLOGY<\/h2>\n\n\n\n

            The primary data is collected from 131 customers from Telangana State using Google Forms. The questionnaire has two parts. The first part contains data collection on demographic profiles. The second part collects specific information about the perception of consumers on digital markets. For general information, nominal and ordinal measurements are used. A few variables are measured through dichotomous questions and the remaining variables are measured through a 5- point Likert scale. The collected data is processed and frequencies, cross-tabulation, and Chi-square tests are used to know if there is any significant difference among male and female customers in the usage of Digital Markets. SPSS 22 version software was used to run the test results.<\/p>\n\n\n\n

            HYPOTHESES OF THE STUDY<\/strong><\/p>\n\n\n\n

            H01: There is no significant difference in the opinion of male and female customers on e-Enjoyment on Digital Markets.<\/em><\/strong><\/p>\n\n\n\n

            H02: There is no significant difference in the opinion of male and female customers on e-Distrust on Digital Markets.<\/em><\/strong><\/p>\n\n\n\n

            H03: There is no significant difference in the opinion of male and female customers on e-Offers offered on Digital Markets.<\/em><\/strong><\/p>\n\n\n\n

            H04: There is no significant difference in the opinion of male and female customers on e-Price on Digital Markets.<\/em><\/strong><\/p>\n\n\n\n

            Attributes of the Study<\/u><\/strong><\/em><\/strong><\/p>\n\n\n\n

            1.e-ENJOYMENT:<\/strong><\/p>\n\n\n\n

              \n
            1. Fun using Website.<\/li>\n\n\n\n
            2. Ease of use.<\/li>\n\n\n\n
            3. Online Variety is more.<\/li>\n\n\n\n
            4. Adequate information on Product quality<\/li>\n\n\n\n
            5. Website Design is attractive.<\/li>\n<\/ol>\n\n\n\n

              2.e-DISTRUST:<\/strong><\/p>\n\n\n\n

                \n
              1. Security Concerns.<\/li>\n\n\n\n
              2. E-Privacy.<\/li>\n\n\n\n
              3. Internet Distrust.<\/li>\n\n\n\n
              4. E-Shopping Risk.<\/li>\n\n\n\n
              5. Return Policy.<\/li>\n<\/ol>\n\n\n\n

                3.e-OFFERS<\/strong><\/p>\n\n\n\n

                  \n
                1. Special Offers attract.<\/li>\n\n\n\n
                2. Online offers vs. Offline offers.<\/li>\n\n\n\n
                3. Brand offer comparison.<\/li>\n\n\n\n
                4. Banner Advertisements.<\/li>\n\n\n\n
                5. Online Product availability.<\/li>\n<\/ol>\n\n\n\n

                  4.e-PRICE:<\/strong><\/p>\n\n\n\n

                    \n
                  1. High Price Risk.<\/li>\n\n\n\n
                  2. Price and Quality comparison.<\/li>\n\n\n\n
                  3. Impulsive Buying.<\/li>\n\n\n\n
                  4. Value for money<\/li>\n\n\n\n
                  5. Price and Quantity<\/li>\n<\/ol>\n\n\n\n

                    Sample Profiling:<\/strong> Data pertaining to 131 consumers comprising different demographic profiles is collected. <\/p>\n\n\n\n

                    RESULTS<\/h2>\n\n\n\n

                    Gender of the consumer<\/strong><\/p>\n\n\n\n

                    Gender<\/strong><\/td>Frequency<\/td>Percent<\/td><\/tr>
                    Valid<\/td>Male<\/td>39<\/td>29.8<\/td><\/tr>
                    Female<\/td>92<\/td>70.2<\/td><\/tr>
                    Total<\/td>131<\/td>100.0<\/td><\/tr><\/tbody><\/table>
                    Table No.1 –<\/strong> Frequency and percentage of male and female consumers<\/figcaption><\/figure>\n\n\n\n

                    From table no.1 it can be observed that the number of female consumers outnumber the males. The male consumers account for 29.8%, whereas the females account for 70.2% of the total respondents.<\/p>\n\n\n\n

                    Age group of the conusmer<\/strong><\/p>\n\n\n\n

                    Age<\/strong><\/td>Frequency<\/td>Percent<\/td><\/tr>
                    Valid<\/td>Less than 25 Years<\/td>8<\/td>6.1<\/td><\/tr>
                    26-35Years<\/td>8<\/td>6.1<\/td><\/tr>
                    36-45 Years<\/td>85<\/td>64.9<\/td><\/tr>
                    More than 45 Years<\/td>30<\/td>22.9<\/td><\/tr><\/tbody><\/table>
                    Table No.2 – <\/strong>Frequency and percentage of the consumers categorized under different age groups<\/figcaption><\/figure>\n\n\n\n

                    Table no.2 depicts the data relating to the age-wise composition of the respondents. The majority of the consumers fall in the age group of 36-45 years with 65% followed by 23% of the respondents who are more than 45 years of age.<\/p>\n\n\n\n

                    Marital Status<\/strong> of the consumer<\/strong><\/p>\n\n\n\n

                    Marital Status<\/strong><\/td>Frequency<\/td>Percent<\/td><\/tr>
                    Valid<\/td>Married<\/td>119<\/td>90.8<\/td><\/tr>
                    Unmarried<\/td>12<\/td>9.2<\/td><\/tr>
                    Total<\/td>131<\/td>100.0<\/td><\/tr><\/tbody><\/table>
                    Table No.3 – <\/strong>Frequency and percentage of the consumers categorized as married and unmarried<\/figcaption><\/figure>\n\n\n\n

                    From table no.3, among the total 131 respondents, 119(91%) are married women and 12(9%) are unmarried.<\/p>\n\n\n\n

                    Monthly Income group of the consumer<\/strong><\/p>\n\n\n\n

                    Monthly Income<\/strong><\/td>Frequency<\/td>Percent<\/td><\/tr>
                    Valid<\/td>Less than Rs.15000<\/td>8<\/td>6.1<\/td><\/tr>
                    15000-25000<\/td>38<\/td>29.0<\/td><\/tr>
                    26000-50000<\/td>34<\/td>26.0<\/td><\/tr>
                    More than 50000<\/td>51<\/td>38.9<\/td><\/tr>
                    Total<\/td>131<\/td>100.0<\/td><\/tr><\/tbody><\/table>
                    Table No.4 – <\/strong>Frequency and percentage of the consumers categorized under various income groups<\/figcaption><\/figure>\n\n\n\n

                    Table no.4 depicts the data relating to the Monthly income-wise composition of the respondents. Majority of the respondents (39%) are earning monthly income more than Rs.50000\/-followed by 55% of the respondents who earn in between Rs.15000 and Rs.50000\/-.<\/p>\n\n\n\n

                    <\/p>\n\n\n\n

                    Reliability test to see the internal consistency in the data<\/strong><\/p>\n\n\n\n

                    Cronbach’s Alpha<\/td>N of Items<\/td><\/tr>
                    0.794<\/td>34<\/td><\/tr><\/tbody><\/table>
                    Table No.5 :Reliability Test Result<\/strong><\/figcaption><\/figure>\n\n\n\n

                    The result from table no. 5 revealed that Cronbach\u2019s Alpha coefficient for 34 items is 0.794, suggesting that the items have relatively high internal consistency and are worthy of retention and hence considered for the study. According to Bland J, Altman, <\/strong>Reliability coefficient of    0.70 or higher is considered \u201cacceptable \u201din most of the social  science research situations.<\/p>\n\n\n\n

                    Opinions of male and female users on \u2018e-Enjoyment’ while using the Digital Markets<\/strong><\/p>\n\n\n\n

                    As per null hypothesis, there is no significant difference in the opinion of the male and female customers on e-Enjoyment while using the Digital Markets. The data has been tested with the help of Chi-Square Test adopting a 5% (0.05) level of significance.<\/p>\n\n\n\n

                     Gender<\/strong><\/td>SD<\/strong><\/td> <\/strong> %<\/strong><\/td>D<\/strong><\/td> <\/strong> %<\/strong><\/td>N<\/strong><\/td> <\/strong> %<\/strong><\/td>A<\/strong><\/td> <\/strong> %<\/strong><\/td>SA<\/strong><\/td> <\/strong> %<\/strong><\/td>Total<\/strong><\/td> <\/strong> %<\/strong><\/td>Chi-Square<\/strong>  Test Results<\/strong><\/td><\/tr>
                    Male<\/strong><\/td>14<\/td>  7.18<\/td>37<\/td>  18.9<\/td>86<\/td>  44.10<\/td>20<\/td>  10.25<\/td>38<\/td>  19.48<\/td>195<\/td>  100<\/td>  Value:40.09 Df:4<\/td><\/tr>
                    Female<\/strong><\/td>74<\/td>  16.08<\/td>76<\/td>  16.52<\/td>111<\/td>  24.13<\/td>117<\/td>  25.43<\/td>82<\/td>  17.8<\/td>460<\/td>  100<\/td><\/tr>
                    Total<\/strong><\/td>88<\/td>  13.4<\/td>113<\/td>  17.25<\/td>197<\/td>  30.07<\/td>137<\/td>  20.9<\/td>120<\/td>  18.32<\/td>655<\/td>  100<\/td><\/tr><\/tbody><\/table>
                    Table No.6: Chi-Square Test on Gender versus ‘e-Enjoyment’ while using the Digital Markets<\/figcaption><\/figure>\n\n\n\n

                    The Chi-Square table value is 9.89 at 4 degrees of freedom. Since the calculated value of Chi-Square is 40.09, which is more than the table value, the null hypothesis is rejected. Hence, there is a significant difference in the opinions of male and female users on \u2018e-Enjoyment while using the Digital Markets\u2019.<\/p>\n\n\n\n

                    Opinions of male and female users on \u2018e-Distrust’ on Digital Markets<\/strong><\/p>\n\n\n\n

                    As per null hypothesis, there is no significant difference in the opinion of male and female customers on e-Distrust on digital markets. The data has been tested with the help of Chi-Square Test adopting 5% (0.05) level of significance. <\/p>\n\n\n\n

                     Gender<\/strong><\/td>SD<\/strong><\/td> <\/strong> %<\/strong><\/td>D<\/strong><\/td> <\/strong> %<\/strong><\/td>N<\/strong><\/td> <\/strong> %<\/strong><\/td>A<\/strong><\/td> <\/strong> %<\/strong><\/td>SA<\/strong><\/td> <\/strong> %<\/strong><\/td>Total<\/strong><\/td> <\/strong> %<\/strong><\/td>  Chi-Square<\/strong>  Test Results<\/strong><\/td><\/tr>
                    Male<\/strong><\/td>14<\/td>7.18<\/td>12<\/td>6.15<\/td>98<\/td>50.25<\/td>7<\/td>3.59<\/td>64<\/td>32.82<\/td>195<\/td>100<\/td>  Value:106.05   Df:4<\/td><\/tr>
                    Female<\/strong><\/td>24<\/td>5.21<\/td>72<\/td>15.65<\/td>90<\/td>19.56<\/td>152<\/td>33.04<\/td>122<\/td>26.52<\/td>460<\/td>100<\/td><\/tr>
                    Total<\/strong><\/td>38<\/td>5.80<\/td>84<\/td>12.82<\/td>188<\/td>28.70<\/td>159<\/td>24.27<\/td>186<\/td>28.39<\/td>655<\/td>100<\/td><\/tr><\/tbody><\/table>
                    Table No.7: Gender versus e-Distrust on Digital markets<\/strong><\/figcaption><\/figure>\n\n\n\n

                    The Chi-Square table value is 9.89 at 4 degrees of freedom. Since the calculated value of Chi-Square is 106.05, which is more than the table value, the null hypothesis is rejected. Hence, there is a significant difference in the opinions of male and female users on \u2018e-Distrust on Digital Markets\u2019.<\/p>\n\n\n\n

                    Opinions of male and female customers on e-Offers at Digital Markets<\/strong>.<\/strong><\/p>\n\n\n\n

                    As per null hypothesis, there is no significant difference in the opinion of male and female customers on e-Offers. T<\/em>he data has been tested with the help of Chi-Square Test adopting 5% (0.05) level of significance. <\/p>\n\n\n\n

                     Gender<\/strong><\/td>SD<\/strong><\/td> <\/strong> %<\/strong><\/td>D<\/strong><\/td> <\/strong> %<\/strong><\/td>N<\/strong><\/td> <\/strong> %<\/strong><\/td>A<\/strong><\/td> <\/strong> %<\/strong><\/td>SA<\/strong><\/td> <\/strong> %<\/strong><\/td>Total<\/strong><\/td> <\/strong> %<\/strong><\/td>Chi-Square<\/strong>  Test Results<\/strong><\/td><\/tr>
                    Male<\/strong><\/td>8<\/td>4.12<\/td>51<\/td>26.13<\/td>103<\/td>52.82<\/td>12<\/td>6.15<\/td>21<\/td>10.76<\/td>195<\/td>100<\/td>  Value:28.49 Df:4<\/td><\/tr>
                    Female<\/strong><\/td>34<\/td>7.39<\/td>79<\/td>17.17<\/td>193<\/td>41.95<\/td>88<\/td>19.13<\/td>66<\/td>14.34<\/td>460<\/td>100<\/td><\/tr>
                    Total<\/strong><\/td>42<\/td>6.41<\/td>130<\/td>19.84<\/td>296<\/td>45.19<\/td>100<\/td>15.26<\/td>87<\/td>13.28<\/td>655<\/td>100<\/td><\/tr><\/tbody><\/table>
                    Table No.8: Gender versus e-Offers offered on Digital Markets<\/strong><\/figcaption><\/figure>\n\n\n\n

                    The Chi-Square table value is 9.89 at 4 degrees of freedom. Since the calculated value of Chi-Square is 28.49, which is more than the table value, the null hypothesis is rejected. Hence, there is a significant difference in the opinions of male and female users on \u2018e-Offers offered on Digital Markets\u201d.<\/p>\n\n\n\n

                    Opinions of the male and female customers on e-Price at Digital Markets<\/strong><\/p>\n\n\n\n

                    As per null hypothesis, there is no significant difference in the opinion of the male and female customers on e-Price offered at Digital Markets. The data has been tested with the help of Chi-Square Test adopting 5% (0.05) level of significance. <\/p>\n\n\n\n

                     Gender<\/strong><\/td>SD<\/strong><\/td>%<\/strong><\/td>D<\/strong><\/td>%<\/strong><\/td>N<\/strong><\/td>%<\/strong><\/td>A<\/strong><\/td>%<\/strong><\/td>SA<\/strong><\/td>
                    %<\/strong><\/td>                    		TOTAL<\/strong><\/td>Chi-Square Test Results<\/strong><\/td><\/tr>
                    Male<\/strong><\/td>33<\/td>16.92<\/td>26<\/td>13.33<\/td>121<\/td>62.05<\/td>11<\/td>5.64<\/td>4<\/td>2.05<\/td>195<\/td>  Value:25.97 Df:4<\/td><\/tr>
                    Female<\/strong><\/td>58<\/td>12.60<\/td>130<\/td>28.26<\/td>211<\/td>45.86<\/td>34<\/td>7.39<\/td>27<\/td>5.86<\/td>460<\/td><\/tr>
                    Total<\/strong><\/td>91<\/td>13.8<\/td>156<\/td>23.81<\/td>332<\/td>50.68<\/td>45<\/td>6.87<\/td>31<\/td>4.73<\/td>655<\/td><\/tr><\/tbody><\/table>
                    Table No.9: Gender<\/strong> versus e-Price offered at Digital Markets<\/strong><\/figcaption><\/figure>\n\n\n\n

                    The Chi-Square table value is 9.89 at 4 degrees of freedom. Since the calculated value of Chi-Square is 25.97, which is more than the table value, the null hypothesis is rejected. Hence, there is a significant difference in the opinions of male and female users on \u2018e-Price offered at Digital Markets\u201d.<\/em><\/p>\n\n\n\n

                    Ranking based on Weighted Mean to identify highest difficulty ratings<\/strong><\/p>\n\n\n\n

                    In order to find out the variables that have the highest difficulty ratings, weighted scores of the \u2018Likert\u2019 scale have been calculated. The ranges to measure the constraints are grouped based on scales developed by Brown (2010). The statement wise weighted score for difficulty level is calculated using the following formula:<\/p>\n\n\n\n

                    [N<\/em>(SD) * (1) + N<\/em>(D) * (2) + N<\/em>(N) * 3 +N<\/em>(A) * (4) + N<\/em>(SA)*5]\/N<\/em>(R)]<\/p>\n\n\n\n

                    Where N<\/em>(SD) = No. of respondents selecting strongly disagree N<\/em>(D) = No. of respondents selecting disagree N<\/em>(U) = No. of respondents selecting neutral N<\/em>(A) = No. of respondents selecting agree N<\/em>(SA) = No. of respondents selecting strongly agree N<\/em>(R) = Total number of respondents (Kostoulas,2013).<\/p>\n\n\n\n

                    Attributes effecting e-Enjoyment<\/strong><\/p>\n\n\n\n

                     <\/strong> S.no.<\/strong><\/td>Attributes<\/strong><\/td>SD<\/strong><\/td>D<\/strong><\/td>N<\/strong><\/td>A<\/strong><\/td>SA<\/strong><\/td>Weighted<\/strong>  Average<\/strong><\/td>Ranking*<\/strong><\/td><\/tr>
                     <\/td>Its fun using website<\/strong><\/td>4<\/td>19<\/td>29<\/td>29<\/td>50<\/td>3.78<\/td>1<\/td><\/tr>
                     <\/td>Ease of use<\/strong><\/td>19<\/td>18<\/td>47<\/td>26<\/td>21<\/td>3.09<\/td>2<\/td><\/tr>
                     <\/td>Online variety<\/strong><\/td>22<\/td>18<\/td>48<\/td>18<\/td>25<\/td>3.05<\/td>3<\/td><\/tr>
                     <\/td>Product quality information<\/strong><\/td>12<\/td>30<\/td>42<\/td>34<\/td>13<\/td>3.05<\/td>4<\/td><\/tr>
                     <\/td>Website design<\/strong><\/td>31<\/td>28<\/td>31<\/td>22<\/td>19<\/td>2.77<\/td>5<\/td><\/tr><\/tbody><\/table>
                    Table No.11 \u2013 Weighted mean and ranking of various attributes of e-Enjoyment<\/strong>. *Data has been sorted based on the weighted average and then ranked.<\/figcaption><\/figure>\n\n\n\n

                    From table no.11, it is evident that out of all the factors, the factor which contributes mostly is, \u2018Its fun of using the website\u2019, which is ranked first, which is followed by \u2018ease of use\u2019 which is ranked second. \u2018Online variety\u2019 was ranked third, and \u2018Product quality information\u2019 was ranked fourth.    \u2018website design\u2019 is the last ranked factor among all the factors in the e-enjoyment.<\/p>\n\n\n\n

                    Attributes influencing the e-distrust<\/strong><\/p>\n\n\n\n

                     <\/strong> S.no.<\/strong><\/td>Attributes<\/strong><\/td>SD<\/strong><\/td>D<\/strong><\/td>N<\/strong><\/td>A<\/strong><\/td>SA<\/strong><\/td>Weighted<\/strong>  Average<\/strong><\/td>Ranking*<\/strong><\/td><\/tr>
                     <\/td>Security concerns<\/strong><\/td>6<\/td>11<\/td>40<\/td>27<\/td>47<\/td>3.75<\/td>1<\/td><\/tr>
                     <\/td>e-Privacy<\/strong><\/td>8<\/td>12<\/td>31<\/td>41<\/td>39<\/td>3.69<\/td>2<\/td><\/tr>
                     <\/td>Internet Distrust<\/strong><\/td>8<\/td>11<\/td>32<\/td>43<\/td>37<\/td>3.69<\/td>2<\/td><\/tr>
                     <\/td>e-Shopping risk<\/strong><\/td>5<\/td>22<\/td>43<\/td>29<\/td>32<\/td>3.47<\/td>4<\/td><\/tr>
                     <\/td>Return policy<\/strong><\/td>11<\/td>28<\/td>42<\/td>19<\/td>31<\/td>3.24<\/td>5<\/td><\/tr><\/tbody><\/table>
                    Table No.12 \u2013 Weighted mean and ranking of various<\/strong> attributes that can influence<\/strong> e-Distrust<\/strong>. *Data has been sorted based on the weighted average and then ranked.<\/figcaption><\/figure>\n\n\n\n

                    From table no.12 it can be analyzed that the most important parameter which influenced on the respondents was found to be the security concerns, which has the highest weighted average of 3.75. The next two important factors, which caused distrust were privacy and internet distrust, which were ranked second and fourth respectively. The last ranked factor was the return policy which also contributed to e-distrust.<\/p>\n\n\n\n

                    Attributes effecting e-Offers<\/strong> <\/p>\n\n\n\n

                     <\/strong> S.no.<\/strong><\/td>Attributes<\/strong><\/td>SD<\/strong><\/td>D<\/strong><\/td>N<\/strong><\/td>A<\/strong><\/td>SA<\/strong><\/td>Weighted<\/strong>  Average<\/strong><\/td>Ranking*<\/strong><\/td><\/tr>
                     <\/td>Special Offers<\/strong><\/td>6<\/td>21<\/td>52<\/td>35<\/td>17<\/td>3.27<\/td>1<\/td><\/tr>
                     <\/td>Online Vs. Offline offers<\/strong><\/td>8<\/td>15<\/td>75<\/td>12<\/td>21<\/td>3.18<\/td>2<\/td><\/tr>
                     <\/td>Brand Comparison<\/strong><\/td>8<\/td>28<\/td>58<\/td>14<\/td>23<\/td>3.12<\/td>3<\/td><\/tr>
                     <\/td>Banner Ads<\/strong><\/td>6<\/td>37<\/td>57<\/td>18<\/td>13<\/td>2.96<\/td>4<\/td><\/tr>
                     <\/td>Online Product Availability<\/strong><\/td>14<\/td>29<\/td>54<\/td>21<\/td>13<\/td>2.92<\/td>5<\/td><\/tr><\/tbody><\/table>
                    Table No.13 \u2013 Weighted mean and ranking of various<\/strong> <\/strong>attributes that effect e-Offers<\/strong>. *Data has been sorted based on the weighted average and then ranked.<\/figcaption><\/figure>\n\n\n\n

                    From table no.13 it can be clearly understood that the most common and ranked first attribute was special offers, and then it is the online offers with a weighted average of 3.18 followed by brand comparison and product availability as the last ranked factor with a weighted average of 2.92.<\/p>\n\n\n\n

                    Attributes effecting e-Price<\/strong><\/p>\n\n\n\n

                     <\/strong> S.no.<\/strong><\/td>Attributes<\/strong><\/td>SD<\/strong><\/td>D<\/strong><\/td>N<\/strong><\/td>A<\/strong><\/td>SA<\/strong><\/td>Weighted<\/strong>  Average<\/strong><\/td>Ranking*<\/strong><\/td><\/tr>
                     <\/td>High Price Risk<\/strong><\/td>7<\/td>34<\/td>66<\/td>11<\/td>13<\/td>2.92<\/td>1<\/td><\/tr>
                     <\/td>Price Vs. Quality<\/strong><\/td>6<\/td>30<\/td>75<\/td>14<\/td>6<\/td>2.88<\/td>2<\/td><\/tr>
                     <\/td>Impulse  Buying<\/strong><\/td>15<\/td>28<\/td>74<\/td>8<\/td>6<\/td>2.71<\/td>3<\/td><\/tr>
                     <\/td>Value for money<\/strong><\/td>31<\/td>30<\/td>59<\/td>5<\/td>6<\/td>2.43<\/td>4<\/td><\/tr>
                     <\/td>Price Vs. Quantity<\/strong><\/td>32<\/td>34<\/td>58<\/td>7<\/td>0<\/td>2.31<\/td>5<\/td><\/tr><\/tbody><\/table>
                    Table No.14 \u2013 Weighted mean and ranking of various<\/strong> <\/strong>attributes that effect <\/strong>e-Price<\/strong>. *Data has been sorted based on the weighted average and then ranked.<\/figcaption><\/figure>\n\n\n\n

                    From table no. 14, it can be analyzed that the respondents are adopting different methods to get the maximum benefit from the digital markets. The majority of consumers are mostly bothered by the risk associated with online buying which is ranked first among all the factors. The last factor is the quantity they get from digital markets is the least ranked.<\/p>\n\n\n\n

                    <\/p>\n\n\n\n

                    Summary & Findings of the Study<\/h2>\n\n\n\n

                    There were 4 hypotheses that were constructed to test if there is any significant difference in the opinions of men and women on Digital markets. The respondents were given questions on 4 attributes i.e., e-Enjoyment, e-Distrust, e-Offers, and e-Price.<\/p>\n\n\n\n

                     All four null hypotheses have been rejected implying that there is a significant difference in perceptions of males and females on Digital Markets. As all of the hypotheses stand rejected, it may be concluded that there is significant difference in the opinions of the respondents, either male or female on e-Enjoyment, e- Distrust, e-Offers, and e-Price offered on digital markets.<\/p>\n\n\n\n

                    The Weighted average calculated shows that the most important factors which impact the online buying behavior of consumers in the digital markets is, the fun in using the website, the security concerns, the special offers given online, and the risk of purchasing online. If all the issues are addressed, then the consumers will be more satisfied and happy transacting online.<\/p>\n\n\n\n

                    REFERENCES<\/h2>\n\n\n\n
                      \n
                    1. Erik Qualman (2013), Socialonomics, How Social Media Transforms the Way We Live and Do Business, John Wiley and Sons.<\/li>\n\n\n\n
                    2. Lages, Luis Filipe Lages Jos\u00e9 Lu\u00edsAbrantes Cristiana Raquel Lages, (2008),”The STRATADPT scale\u201d, International Marketing Review, Vol. 25 Iss 5 pp. 584 \u2013 600.<\/li>\n\n\n\n
                    3. Chitra Sharma, \u201cConsumer buying behaviour towards online shopping \u2013 A Review of literature\u201d, Indian journal of applied research, Volume (5), Issue: 4, April 2015, ISSN – 2249-555X.<\/li>\n\n\n\n
                    4. Bandyopadhyay, D. Sen, J. (2011) Internet of Things: Applications and Challenges in Technology and Standardization, Wireless Personal Communications, Volume 58, Issue 1, pp 49\u201369.<\/li>\n\n\n\n
                    5. Venkatesh, V. and Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Science, 39 (2), 273-312.<\/li>\n\n\n\n
                    6. Abdul Brosekhan and C. MuthuVelayutham, 2008, An Empirical Study on Consumers Buying Behaviour towards Selected Home Appliance Products in Ramanathapuram, IOSR Journal of Business and Management (IOSR-JBM), PP 13-21.<\/li>\n\n\n\n
                    7. Kotler, P. and Armstrong, G. (2012) Principles of Marketing. 14th Edition, Pearson Education Limited, Essex, England.<\/li>\n\n\n\n
                    8. Shankar, Venkatesh and Inman, Jeffrey and Mantrala, Murali and Kelley, J. Eileen and Rizley, Ross, Innovations in Shopper Marketing: Current Insights and Future Research Issues (2011). Journal of Retailing 87S (1, 2011) S29-S42; Mays Business School Research Paper No. 2012-59.<\/li>\n\n\n\n
                    9. Kwak, H., Lee, C., Park, H. and Moon, S. (2010) What Is Twitter, a Social Network or a News Media? ACM: Proceedings of the 19th International Conference on World Wide Web, New York, 591-600.<\/li>\n\n\n\n
                    10. Foux, G., (2006). Consumer-generated media: Get your customers involved. Brand Strategy, pg. no.38-39.<\/li>\n\n\n\n
                    11. Russell S. Winer (2009), New Communications Approaches in Marketing: Issues and Research Directions, Journal of Interactive Marketing,Vol. 23,No.2, Pp-108-117.<\/li>\n\n\n\n
                    12. Bhaskar Kumar,\u201d Impact Of Digital Marketing And E-Commerce On The Real Estate Industry,\u201d Impact: International Journal Of Research In Business Management, Vol. 2, Issue 7, Jul 2014, 1722.<\/li>\n<\/ol>\n\n\n\n
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                      Article Metrics An Analytical Study on Consumer Behavior Pertaining to e-Markets Madhuri Thakur Telangana Mahila Vishwa Vidhyalayam, Koti, Hyderabad, Telangana. ABSTRACT As the world is moving towards digitalization, the e-commerce sector has experienced a big jump in a short tenure. This has increased the number of e-commerce companies competing with each other to capture the market […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-55","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/commerce-management.kmics.ac.in\/index.php\/wp-json\/wp\/v2\/pages\/55","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/commerce-management.kmics.ac.in\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/commerce-management.kmics.ac.in\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/commerce-management.kmics.ac.in\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/commerce-management.kmics.ac.in\/index.php\/wp-json\/wp\/v2\/comments?post=55"}],"version-history":[{"count":5,"href":"https:\/\/commerce-management.kmics.ac.in\/index.php\/wp-json\/wp\/v2\/pages\/55\/revisions"}],"predecessor-version":[{"id":205,"href":"https:\/\/commerce-management.kmics.ac.in\/index.php\/wp-json\/wp\/v2\/pages\/55\/revisions\/205"}],"wp:attachment":[{"href":"https:\/\/commerce-management.kmics.ac.in\/index.php\/wp-json\/wp\/v2\/media?parent=55"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}