Pub Date : 2021-09-30DOI: 10.17010/ijom/2021/v51/i9/166162
P. Balakrishnan Menon
{"title":"Influence of Social Media Marketing Efforts on Brand Equity and Consumer Response to Branded Shoes in India","authors":"P. Balakrishnan Menon","doi":"10.17010/ijom/2021/v51/i9/166162","DOIUrl":"https://doi.org/10.17010/ijom/2021/v51/i9/166162","url":null,"abstract":"","PeriodicalId":38358,"journal":{"name":"Indian Journal of Marketing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43937556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-30DOI: 10.17010/ijom/2021/v51/i9/166163
Sarfaraz Javed, U. Husain, N. Pathak
{"title":"Relevance of Financial Service Advertisements in Investment Decisions and Purchase of Financial Products : Evidence from the Indian Insurance Sector","authors":"Sarfaraz Javed, U. Husain, N. Pathak","doi":"10.17010/ijom/2021/v51/i9/166163","DOIUrl":"https://doi.org/10.17010/ijom/2021/v51/i9/166163","url":null,"abstract":"","PeriodicalId":38358,"journal":{"name":"Indian Journal of Marketing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47679105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-30DOI: 10.17010/ijom/2021/v51/i9/166161
Charru Hasti, S. Arora, Amit Mehndiratta, M. Sagar, H. Chaudhry
{"title":"Profile Centric Community Awareness and Engagement for Adolescent Girls : An Empirical Study on Early Marriage in India","authors":"Charru Hasti, S. Arora, Amit Mehndiratta, M. Sagar, H. Chaudhry","doi":"10.17010/ijom/2021/v51/i9/166161","DOIUrl":"https://doi.org/10.17010/ijom/2021/v51/i9/166161","url":null,"abstract":"","PeriodicalId":38358,"journal":{"name":"Indian Journal of Marketing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42304770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-31DOI: 10.17010/ijom/2021/v51/i8/165760
Smitha Siji
{"title":"Social Commerce of Indian Customers : Role of Social Media Usage","authors":"Smitha Siji","doi":"10.17010/ijom/2021/v51/i8/165760","DOIUrl":"https://doi.org/10.17010/ijom/2021/v51/i8/165760","url":null,"abstract":"","PeriodicalId":38358,"journal":{"name":"Indian Journal of Marketing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46560333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-31DOI: 10.17010/ijom/2021/v51/i8/165761
Hansini Premi, Monica Sharma, G. S. Dangayach
{"title":"Green Marketing : A Systematic Literature Review","authors":"Hansini Premi, Monica Sharma, G. S. Dangayach","doi":"10.17010/ijom/2021/v51/i8/165761","DOIUrl":"https://doi.org/10.17010/ijom/2021/v51/i8/165761","url":null,"abstract":"","PeriodicalId":38358,"journal":{"name":"Indian Journal of Marketing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48685647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-31DOI: 10.17010/ijom/2021/v51/i8/165759
Bhaskar Roy, D. Bera, P. K. Tripathi, S. Upadhyay
{"title":"Improving Profitability Using Predictive Analytics","authors":"Bhaskar Roy, D. Bera, P. K. Tripathi, S. Upadhyay","doi":"10.17010/ijom/2021/v51/i8/165759","DOIUrl":"https://doi.org/10.17010/ijom/2021/v51/i8/165759","url":null,"abstract":"","PeriodicalId":38358,"journal":{"name":"Indian Journal of Marketing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41788139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-31DOI: 10.17010/IJOM/2021/V51/I5-7/163886
Sunita Guru, Nityesh Bhatt, Nishant Agrawal
In the past two decades, the e-commerce industry has grown exponentially. With new customers being on board everyday, understanding customer behavior has become more relevant than ever before. Trust is a vital aspect in the online shopping experience as it significantly influences consumer behavior, including the selection of e-commerce brands. Thorough literature review has yielded three dimensions of online trust, that is, ability, benevolence, and integrity, but no literature was found on whether there could be any prioritization among the three dimensions. This study used confirmatory factor analysis to verify the predefined scale of dimensions of online trust and analytical hierarchy process (AHP) technique to prioritize and solve a problem that required the multi-criteria decision making. The seminal contribution of this paper is ranking the three dimensions of online trust as identified in literature. The results affirmed that ability is the top ranked dimension of online trust for e-commerce brands that drives online shoppers followed by benevolence and integrity.
{"title":"Prioritization of Dimensions of Online Trust Using Analytical Hierarchy Approach","authors":"Sunita Guru, Nityesh Bhatt, Nishant Agrawal","doi":"10.17010/IJOM/2021/V51/I5-7/163886","DOIUrl":"https://doi.org/10.17010/IJOM/2021/V51/I5-7/163886","url":null,"abstract":"In the past two decades, the e-commerce industry has grown exponentially. With new customers being on board everyday, understanding customer behavior has become more relevant than ever before. Trust is a vital aspect in the online shopping experience as it significantly influences consumer behavior, including the selection of e-commerce brands. Thorough literature review has yielded three dimensions of online trust, that is, ability, benevolence, and integrity, but no literature was found on whether there could be any prioritization among the three dimensions. This study used confirmatory factor analysis to verify the predefined scale of dimensions of online trust and analytical hierarchy process (AHP) technique to prioritize and solve a problem that required the multi-criteria decision making. The seminal contribution of this paper is ranking the three dimensions of online trust as identified in literature. The results affirmed that ability is the top ranked dimension of online trust for e-commerce brands that drives online shoppers followed by benevolence and integrity.","PeriodicalId":38358,"journal":{"name":"Indian Journal of Marketing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45815911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E-commerce companies have started betting big on small towns to look for business opportunities, having a large customer base, and focusing on making customers to buy online as much as possible. Therefore, the study aimed to explore different dimensions of customers’ impulse buying behaviour (IBB) in an online buying environment, and further validating the extracted determinants in small towns of North India. The study included a sample of 304 small town online buyers and used exploratory factor analysis (EFA) to explore the key factors, which resulted into five key determinants of IBB ; Hedonic Online Shopping Motivation, Marketing Stimulus, Impulse Buy, Impulse Buying Tendency, and Situation Stimulus. Further, confirmatory factor analysis (CFA) was performed to evaluate validity and reliability of the identified variables of IBB. The findings revealed good internal consistency among the constructs. Additionally, all measurement model fit indices were found to be under the acceptance range, which indicated no validity concerns in the measurement model. The study provided insights on the complex impulse buying behavior of small town online consumers, which completely differs from the buyers of metro cities. Therefore, the identified determinants of customers’ online IBB will benefit e-tailors by providing them a platform to understand the buying behavior of small town consumers for designing their marketing strategies to serve them better and to gain over the competition in the e-marketplace.
{"title":"Online Impulse Buying Behaviour of Indian Small Town Consumers : Scale Development and Validation","authors":"Anuradha Agarwal, Bhawna Chahar, Narender Singh Bhati","doi":"10.17010/IJOM/2021/V51/I5-7/161647","DOIUrl":"https://doi.org/10.17010/IJOM/2021/V51/I5-7/161647","url":null,"abstract":"E-commerce companies have started betting big on small towns to look for business opportunities, having a large customer base, and focusing on making customers to buy online as much as possible. Therefore, the study aimed to explore different dimensions of customers’ impulse buying behaviour (IBB) in an online buying environment, and further validating the extracted determinants in small towns of North India. The study included a sample of 304 small town online buyers and used exploratory factor analysis (EFA) to explore the key factors, which resulted into five key determinants of IBB ; Hedonic Online Shopping Motivation, Marketing Stimulus, Impulse Buy, Impulse Buying Tendency, and Situation Stimulus. Further, confirmatory factor analysis (CFA) was performed to evaluate validity and reliability of the identified variables of IBB. The findings revealed good internal consistency among the constructs. Additionally, all measurement model fit indices were found to be under the acceptance range, which indicated no validity concerns in the measurement model. The study provided insights on the complex impulse buying behavior of small town online consumers, which completely differs from the buyers of metro cities. Therefore, the identified determinants of customers’ online IBB will benefit e-tailors by providing them a platform to understand the buying behavior of small town consumers for designing their marketing strategies to serve them better and to gain over the competition in the e-marketplace.","PeriodicalId":38358,"journal":{"name":"Indian Journal of Marketing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46690496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-31DOI: 10.17010/IJOM/2021/V51/I5-7/163887
R. Shankar
This short communication is an extract from a major research work on understanding tourists’ experience and post touring behaviour and this portion intended to highlight the role of travel partners in tourists’ emotional experience at a destination. Intensive review of literary sources confirmed that this aspect was untapped and lacked research insights. A structured questionnaire containing the statements measuring tourists’ emotional experience and travel partners was floated to 400 tourists using convenient sampling technique. The data collection instrument also had questions on tourists’ personal factors. Sample size was rounded to 370 after removing the illegible responses. A multivariate analysis approach was employed to test whether travel partners influenced tourists’ emotional experience at a destination. The findings revealed that the emotional aspects such as pleasantness, excitement, calmness, happiness, energetic, friendly, and surprising were influenced by the tourists’ travel partners.
{"title":"Do Travel Partners Influence the Emotional Experience of Tourists at Destinations ? A Short Communication","authors":"R. Shankar","doi":"10.17010/IJOM/2021/V51/I5-7/163887","DOIUrl":"https://doi.org/10.17010/IJOM/2021/V51/I5-7/163887","url":null,"abstract":"This short communication is an extract from a major research work on understanding tourists’ experience and post touring behaviour and this portion intended to highlight the role of travel partners in tourists’ emotional experience at a destination. Intensive review of literary sources confirmed that this aspect was untapped and lacked research insights. A structured questionnaire containing the statements measuring tourists’ emotional experience and travel partners was floated to 400 tourists using convenient sampling technique. The data collection instrument also had questions on tourists’ personal factors. Sample size was rounded to 370 after removing the illegible responses. A multivariate analysis approach was employed to test whether travel partners influenced tourists’ emotional experience at a destination. The findings revealed that the emotional aspects such as pleasantness, excitement, calmness, happiness, energetic, friendly, and surprising were influenced by the tourists’ travel partners.","PeriodicalId":38358,"journal":{"name":"Indian Journal of Marketing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44345081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-31DOI: 10.17010/IJOM/2021/V51/I5-7/161644
S. Sharma, Gautam Dutta
Films are a high-risk industry. Accurate prediction of movie box-office revenues can reduce this market risk and inform the investment decisions regarding promotion of the movie closer to a film’s release or right after release. Studies have shown that chatter on social media platforms like Twitter along with certain movie-related factors can be useful in predicting success of movies. Sentiment of tweets for any movie gives important information about the consumer’s reaction and the polarity of these sentiments has been shown to have an impact on prediction of box-office revenues. This paper presented a novel Bollywood domain specific sentiment lexicon that delivered state-of-the-art performance for polarity determination of reviews. SentiDraw lexicon was built on movie reviews scraped from IMDB and calculated the sentiment orientation of these words by calculating the probability distribution of words across reviews with different star ratings. The results showed that SentiDraw lexicon delivered a superior performance compared to any other lexicon-based method. This significantly contributed in enhancing the prediction accuracy of box office for movies using textual data from Twitter for analysis. In fact, this study demonstrated an extremely parsimonious regression model that used only budget, hype factor, tweet volume, and polarity of tweets for a robust prediction of box office revenues even before the release of a movie.
{"title":"Prediction of Box Office for Bollywood Movies Using State-of-the-Art SentiDraw Lexicon for Twitter Analysis","authors":"S. Sharma, Gautam Dutta","doi":"10.17010/IJOM/2021/V51/I5-7/161644","DOIUrl":"https://doi.org/10.17010/IJOM/2021/V51/I5-7/161644","url":null,"abstract":"Films are a high-risk industry. Accurate prediction of movie box-office revenues can reduce this market risk and inform the investment decisions regarding promotion of the movie closer to a film’s release or right after release. Studies have shown that chatter on social media platforms like Twitter along with certain movie-related factors can be useful in predicting success of movies. Sentiment of tweets for any movie gives important information about the consumer’s reaction and the polarity of these sentiments has been shown to have an impact on prediction of box-office revenues. This paper presented a novel Bollywood domain specific sentiment lexicon that delivered state-of-the-art performance for polarity determination of reviews. SentiDraw lexicon was built on movie reviews scraped from IMDB and calculated the sentiment orientation of these words by calculating the probability distribution of words across reviews with different star ratings. The results showed that SentiDraw lexicon delivered a superior performance compared to any other lexicon-based method. This significantly contributed in enhancing the prediction accuracy of box office for movies using textual data from Twitter for analysis. In fact, this study demonstrated an extremely parsimonious regression model that used only budget, hype factor, tweet volume, and polarity of tweets for a robust prediction of box office revenues even before the release of a movie.","PeriodicalId":38358,"journal":{"name":"Indian Journal of Marketing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42566316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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