Indian Journal of Computer Science

Open Access Open Access  Restricted Access Subscription Access

Modeling the Antecedents of Customer Engagement With Health Related Content


Affiliations
1 Research Scholar, School of Management, The Northcap University, Near Rotary Public School, Cartarpuri Alias, Huda, Sector 23A, Gurugram, Haryana, 122 017, India
2 Assistant Professor, School of Management, The Northcap University, Near Rotary Public School, Cartarpuri Alias, Huda, Sector 23A, Gurugram, Haryana, 122 017, India

   Subscribe/Renew Journal


This paper seeks to study the interrelationship between different variables impacting customer engagement with health-related content. For this ISM approach has been employed. Variables impacting customer engagement are identified from the existing literature and subsequently modeled in the form of a diagraph (ISM Model). Finally, the variables are classified into different categories based on their dependence and driving power using MICMAC Analysis. Seven variables identified from the literature are readable content, usefulness of content, socialization needs, trustworthiness, complete information, subjectivity, and content novelty. These variables are then modeled into a diagraph (ISM model) showing interrelationship among the variables. Three levels of hierarchy are formed. Where the first level is occupied by three variables namely, usefulness of content, socialization needs, and content novelty. The second level is occupied by three more variables, namely, readable content, trustworthiness, and subjectivity. The third level is occupied by one variable, that is, complete information. Using MICMAC analysis content novelty is classified as autonomous variable, usefulness of content and socialization needs were classified as dependent variables, trustworthiness, and subjectivity are classified as linkage variables, readable content, and complete information being classified as independent variables.

Keywords

Customer Engagement, Health-Related Content, ISM, MICMAC Analysis.
User
Subscription Login to verify subscription
Notifications
Font Size

  • R. J. Brodie, A. Ilic, B. Juric, and L. Hollebeek, “Consumer engagement in a virtual brand community: An exploratory analysis,” J. of Bus. Res., vol. 66, no. 1, pp. 105–114, 2013. [Online]. Available: https://doi.org/10.1016/j.jbusres.2011.07.029

  • R. A. Owusu, C. M. Mutshinda, I. Antai, K. Q. Dadzie, and E. M. Winston, “Which UGC features drive web purchase intent? A spike-and-slab Bayesian variable selection approach,” Internet Res., vol. 26, no. 1, pp. 22–37, 2016. [Online]. Available: https://doi.org/10.1108/IntR-06-2014-0166

  • B. A. Vander Shee, J. Peltier, and A. J. Dahl, “Antecedent consumer factors, consequential branding outcomes and measures of online consumer and measures of online consumer engagement: Current research and future directions,” J. of Res. in Interactive Marketing, vol. 14, no. 2, pp. 239–268, 2020. [Online]. Available: https://doi.org/10.1108/JRIM-01-2020-0010

  • N. Park, K. Kee, and S. Valenzuela, “Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes,” Cyber Psychology Behaviour, vol. 12, no. 6, pp. 729–733, 2009. [Online]. Available: https://doi.org/10.1089/cpb.2009.0003

  • C. Moorman, R. Deshpande, and G. Zaltm, “Factors affecting trust in market research relationships,” J. of Marketing, vol. 57, no. 1, pp. 81–101, 1993. [Online]. Available: https://doi.org/10.1177/002224299305700106

  • L. Liu, M. K. O. Lee, R. Liua, and J. Chen, “Trust transfer in social media brand communities: The role of consumer engagement,” Int. J. of Inform. Manage., vol. 41, pp. 1–13, 2018. https://doi.org/10.1016/j.ijinfomgt.2018.02.006

  • M. R. Habibi, M. Laroche, and M.-O, Richard, “The roles of brand community and community engagement in building brand trust on social media,” Comput. in Human Behavior, 37, pp. 152–161, 2014. [Online]. Available: https://doi.org/10.1016/j.chb.2014.04.016

  • T. M. Luhrmann, “Subjectivity,” Anthropological Theory, vol. 6, no. 3, pp. 345–361, 2006. https://doi.org/10.1177/1463499606066892

  • F. Hill and A. Korhonen, “Concreteness and subjectivity as dimensions of lexical meaning,” Proc. of the 52nd Annual Meeting of the Assoc. for Computational Linguistics (vol. 2: Short Papers), 2014, pp. 725–731.[Online]. Available: http://dx.doi.org/10.3115/v1/P14-2118

  • K. Heinonen, “Positive and negative valence influencing consumer engagement,” J.of Service Theory and Practice, vol. 28, no. 2, pp. 147–169, 2018. [Online]. Available: https://doi.org/10.1108/JSTP-02-2016-0020

  • A. Mendelson, “Effects of novelty in news photographs on attention and memory,” Media Psychology, vol. 3, no. 2, pp.119–157, 2009. [Online]. Available: https://doi.org/10.1207/S1532785XMEP0302_02

  • R. S. Tokunaga, “Engagement with novel virtual environments: The role of perceived novelty and flow in the development of the deficient self-regulation of internet use and media habits,” Human Communication Res., vol. 39, no. 3, pp. 365–393, 2013. [Online]. Available: https://doi.org/10.1111/hcre.12008

  • G. Tumbat, and R. W. Belk, “Marketplace tensions in extraordinary experiences,” J. of Consumer Res., vol. 38, no. 1, pp. 42–61, 2011. [Online]. Available: https://doi.org/10.1086/658220

  • M. Yang, Y. Ren, and G. Adomavicius, “Understanding user-generated content and customer engagement on Facebook business pages,” Inform. Syst. Res., vol. 30, no. 3, pp. 839–855, 2019. [Online]. Available: https://doi.org/10.1287/isre.2019.0834

  • M. Sharma, M. Gupta, and S. Joshi, “Adoption barriers in engaging young consumers in the Omni-channel retailing,” Young Consumers, vol. 21, no. 2, pp. 193–210, 2020. [Online]. Available: https://doi.org/10.1108/YC-02-2019-0953

  • B. L. Neiger, R. Thackeray, S. H. Burton, C. R. Thackeray, and J. H. Reese, “Use of Twitter among local health departments: An analysis of information sharing, engagement, and action,” J. of Medical Internet Res., vol. 15, no. 8, pp. 1–10, 2013. [Online]. Available: https://doi.org/10.2196/jmir.2775

  • J. N. Warfield, “Developing interconnection matrices in structural modeling,” IEEE Trans. on Syst., Man, and Cybern., vol. SMC-4, no. 1, pp. 81–87, 1974. [Online]. Available: https://doi.org/10.1109/TSMC.1974.5408524

  • R. N. Tripathi, “Health auditing and administration using IoT with the help of Cloud,” Indian J. of Comput. Sci., vol. 6, no. 2, pp. 18–22, 2021. [Online]. Available:http://dx.doi.org/10.17010/ijcs%2F2021%2Fv6%2Fi12%2F160695

  • T. Chauhan and R. Nayyar, “An Interpretive Structural Modelling approach for modelling the factors affecting consumer online buying behavior,” Indian J. of Comput. Sci., vol. 5, no. 4&5, pp. 18–25, 2020. [Online]. Available: http://dx.doi.org/10.17010/ijcs%2F2020%2Fv5%2Fi4-5%2F154784


Abstract Views: 166

PDF Views: 0




  • Modeling the Antecedents of Customer Engagement With Health Related Content

Abstract Views: 166  |  PDF Views: 0

Authors

Trishala Chauhan
Research Scholar, School of Management, The Northcap University, Near Rotary Public School, Cartarpuri Alias, Huda, Sector 23A, Gurugram, Haryana, 122 017, India
Shilpa Sindhu
Assistant Professor, School of Management, The Northcap University, Near Rotary Public School, Cartarpuri Alias, Huda, Sector 23A, Gurugram, Haryana, 122 017, India

Abstract


This paper seeks to study the interrelationship between different variables impacting customer engagement with health-related content. For this ISM approach has been employed. Variables impacting customer engagement are identified from the existing literature and subsequently modeled in the form of a diagraph (ISM Model). Finally, the variables are classified into different categories based on their dependence and driving power using MICMAC Analysis. Seven variables identified from the literature are readable content, usefulness of content, socialization needs, trustworthiness, complete information, subjectivity, and content novelty. These variables are then modeled into a diagraph (ISM model) showing interrelationship among the variables. Three levels of hierarchy are formed. Where the first level is occupied by three variables namely, usefulness of content, socialization needs, and content novelty. The second level is occupied by three more variables, namely, readable content, trustworthiness, and subjectivity. The third level is occupied by one variable, that is, complete information. Using MICMAC analysis content novelty is classified as autonomous variable, usefulness of content and socialization needs were classified as dependent variables, trustworthiness, and subjectivity are classified as linkage variables, readable content, and complete information being classified as independent variables.

Keywords


Customer Engagement, Health-Related Content, ISM, MICMAC Analysis.

References





DOI: https://doi.org/10.17010/ijcs%2F2021%2Fv6%2Fi5%2F166515