Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Review Article: 2021 Vol: 25 Issue: 3S

Transition from Traditional Marketing To Digital Marketing: A Bibliometric Analysis

Divya Sharma, Shri Mata Vaishno Devi University, Katra

Citation Information: Sharma, D. (2021). Transition from traditional marketing to digital marketing: a bibliometric analysis. Academy of Marketing Studies Journal, 25(S3), 1-6.

Abstract

Today ourselves, without even a certainty, inside the information age and web marketing and advertising get a massive effects as how customers interact and businesses operate. Flexibility to respond towards this current paradigm. Due to the immense rise of new technologies, the rapid rise in producers and consumers, the lengthening of distribution network and thus the volume of data, just one thing to cope handle significant alteration is to digitize all activities. Despite the fact that current generation of communication has arrived, experts advise businesses to keep conventional tactics in mind to strive to combine internal advertising and conventional strategies to accomplish their objectives.

Introduction

With the changing era, the use of internet technology is rising all the time, with people purchasing and selling goods and services over the internet. To achieve more effectively than the competition, they must have a full awareness of their customer’s wants, from what they are to how they may be met, as well as what new demands they can generate (Raluca,2016). Digital marketing, on the surface, appears to be significantly different from traditional marketing in that it concentrates on the fourth P: Promotion. On the other side, Digital marketing is extremely effective because it includes all four Ps of marketing. Instead, it makes distinctive use of each one often much better than traditional marketing (LYFE Marketing, 2021); (Jamil et al, 2021). The theory and technique of advertising and promotion in new directions, often by distribution platforms, in way to attain customers and users inside a prompt, appropriate, customized, and outlay approach as referred to as digital promotion (Todor, 2016).

The targeted, quantitative, but collaborative promotion of items and/or services uses electronic techniques to achieve, persuade, and engage customers often referred to as digital marketing. The basic goal is to use a variety of digital marketing strategies to promote businesses, establish preferences, and drive purchases (Raluca, 2016). According to forecasts, the digital market would by 2026, the market will have grown from $38.5 billion in 2017 to $200 billion. The main purpose is to promote firms, form preferences and drive sales using a variety of digital marketing tactics (Raluca, 2016). As according projections, the online market is growing between $million by 2021 trillion in 2018 to $1 trillion in 2026. Increasing internet technology, smartphone adoption and the debut of 4G and 5G networks are all important drivers of e-commerce change (http://www.ibef.org.).

With a bibliometric review of the scholarly literature on changing from traditional marketing, the study will be an attempt to answer these broad questions. 1) What is the present state of academic study on the transition from traditional to digital marketing? 2) How has scholarly research influenced both transitions? (Pilsto et al, 2008). Cho & Kang (2006) discovered that internet research has increased considerably over the previous ten years, with contribution from a wide range of disciples and theoretical and methodologies viewpoints. Most quoted publications and papers, along with co-citation behaviors, are used to generate a holistic "picture" of the region, highlighting important impacts and contributors. The bibliometric analysis provides the prospects to establish a “baseline” for the growing area of internet advertising, allowing future academics to understand where the discipline originated and track its evolution through time (Pilsto et al, 2008).

Literature Review

The fundamental assumptions of the study are the frequency with which a set of papers or patents was cited is a measure of the impact of the set of papers. (Pilato et al; 2008) stated in their through assessing the most-cited authors, publications, and the general structure of the disciplines, the study indicates how scholarly research has impacted the Online Marketing areas and answers broad topics using a co - citation examination of published literature on online ads.. It was also observed that literature bibliometric over the patent bibliometric was highly skewed, with small number of high -impact and large number of minimal impact on patents and papers. . The study (Chaffey & Palron; 2012) examined over how companies may utilise web techniques to understand their digital distribution methods for implementing techniques for setting up digital advertising improvement initiatives, but also how individuals, strategy, assessment, and technology all can be combined. According to the study (Gupta et.al; 2020), which used the database Scopus to provide a quantitative and qualitative assessment of e-commerce research in India in a global context, India is considered the 6th most productive country, as the study profiles top 15 global organisations, top 15 global authors, top subject areas, and top channels in research communications and provides a brief bibliometric analysis on high – cited papers. (Zhou et.al; 2020) assessed the bibliometric analysis used to identify the characteristics and evolution of social commerce research from 2003 to 2018, using the database Web of Science. The study found that the social commerce domain had a lot of cooperation research in the form of multi-authored publications, and the key research topics may be differentiated based on the LLR ratio: 1) The evolution of social trade. 2) The relationship between the customer and the vendor 3) in the context of social buying, consumer trust is important. The study's conclusions revealed existing understanding based on commerce and system development views. The study's findings include seven perspectives on entrepreneurship's digitisation, covering topics such as digital economy, fundraising, and the collaborative economy (Kraus, et al; 2019; Veklenko; 2020) demonstrates a deep understanding of factors which drove ultimate purchasing intention along with short and medium and lengthy efficacy.. The study's goal is to uncover key drivers and outline current and developing research patterns in academic literature on the efficacy of online advertising. The study's findings suggested that the online advertising domain is heavily influenced by technological advancements and is increasingly focused on interdisciplinarity, providing new insights to online marketers on the key determinants of online advertising effectiveness and effectiveness estimation models. Chung et.al, 2020 the development of innovation management study was investigated using a co - citation approach with visually mapping, which included citation analysis and the determination of significant citations, contributors, and highest publications.. The main research stream in digital innovation research is also done, as is cluster analysis. Based on co-citations, the study discovered four thematic clusters and seven thematic clusters based on bibliometric analysis. The study aim to map the state of digital market studies by using bibliometric and analysed research data methods such as Scopus and Vos Viewer and the findings shows which country, research institutions and individual researchers are more productive in digital studies (Nangoy et.al, 2020). (Kraus et.al, 2019) were stated the special issue on the conclusions of the study, Virtual Innovations and Experimentation, contain seven articles on the digitization of innovation, covering major subjects such as fintech, fundraising, and the collaborative economy. (Liu et al, 2017) were investigated publishers and highest articles are discovered in the increase of online innovation process that used a co - citation methodology with graphical mappings and assessed though bibliometric analysis and important references. The main research stream in digital innovation research and cluster analysis is also conducted. (Verma 2021) were stated the aim to perform citation trend, context, keyword and co-citation analysis on digital marketing published over the last few years from the database Scopus with 296 publications out of which 286 publications were considered for the study. Vos Viewer and Voyant tool is used to analyze the data. The result of the study indicates the maximum research on digital marketing in the year 2019 and observed the maximum citations on publications in 2017 and the highest number of citation per publication in year 2010-2013. The bibliometric study is used to assess the 12-year published articles in core DMC related journals. The study provided scholars with theme insights and consequences that really are prospective pathways for developing successful DMC. (Almahri et al, 2021) were studied the latest academic research trends on e- commerce themes in China and provided the guidelines of interest to the government, IT policy makers and to academic researchers. The study identified the need to focus on such topics and themes wherein China had been left behind from other countries in e-commerce.

Role of Chatbots in Advertisement

Microsoft introduced the concept of conversations as a platform in early spring 2016, where artificial intelligence (AI) and natural language interaction enables new ways to communicate with interactive technology (Folstad & Brandtzaeg, 2017). Many commercial and private domains have been conquered by domain specific assistance in the shape of chatbots (de Reuver et.al, 2020). Facebook introduced tools for designing chatbots for Messenger, its messaging programme, at the same time (Folstad & Brandtzaeg, 2017). Also there's Google Search, a robot built into the Allo chat app, and system updates of the Android platform, that excels almost all, but with some clumsiness. The shift between providing tremendous and interface dynamics to conversational style for robots be represented by development for robots. Developers gain with true freedom regarding characteristics is defined and engagement approaches in the contemporary generation of graphical interfaces, enabling for complete representation of dynamic network components. The word “chatbots” consists of the term “chat” and “robot”. Initially, the word "chatbot" related to software applications that mimicked human discourse using a content communication network. Chatbots feature a textual or input overlay which offers flexibility customers to connect with the technology powering it in instantaneously (Wang & Petrina, 2013). Chatbots, also known as multilingual robots, have now been created for a number of uses. Artificially intelligent (AI) and text analytics (NLP) advancements are changing ways virtual helpers cooperate alongside people (Ngugen & Sidorova, 2018; Jain et.al, 2018). Since content and monologue interaction have advanced, robots are grown more accessible and common (Bittner et.al, 2019). Some of the new smart assistants such as Siri, Google Now, Cortane, Fcebook M, Blackberry Assistant, Braina, Tenco, Speaktoit Assistant, Hound, Amazon Echo (Alexa) were created with the goal of assisting people in their daily lives as voice – activated intelligent personal assistants. Chatbot have grown expontially as a result of the emergence of these services (Janssen et al, 2017). The ability of robots and interface design that employ natural language to facilitate conversational activities while giving usable output is a critical success factor. Google Assistant, with its capacity to maintain a conversational stream through multiple phrases is a dialogue, is arguably the current state of the art. Chatbots can have a wide range of properties, prompting the use of terms such multilingual agents, chatterbots and virtual assistants. In this analysis, the word “chatbot” refer to an autonomous conversational entity that engages purpose or task – oriented discourse via a text- based environment (Chaves & Gerosa, 2020). As a consequence of robotics, working is largely characterised by the interplay of humans and robots (Lehrer et al, 2018; Schneider et.al, 2018). Simultaneously, linguistic bots may be developed with the objective of influencing perceptions (Fogg, 2002; Mirsch et al, 2017; Oinas-Kukkonen & Harjumaa, 2009; Weinmann et.al, 2016). In this pretty extreme viewpoint, people are considered as "artworks molded and utilized by the (system of) technologies instead of conversely" (Demetis & Lee, 2018). As a result, in addition to variable degrees of (1) engagement, (2) cognition, and (3) individual action. A chatbot is a piece of software communicates with humans by using natural language processing and predictive analysis to understand queries and respond appropriately (Mittal et.al, 2016). Chatbots have become increasingly popular in real-world applications due to their ability to precisely mimic human representatives during conversations. Chatbots are available 24 hours a day, seven days a week and cost less than humans (Mittal et al, 2016).

Role of Virtual Assistants in Advertisement

A lot of tech companies have introduced devices for consumers that support voice assistant technology these days. According to a recent study undertaken by the global auditing firm PWC, a survey of 1000 consumers aged 18 to 64 years old was conducted to get awareness about virtual assistant technology. Now a days, virtual assistant and voice – controlled devices are used by 100,000 people in their routine life. Most useable artificial intelligent assistants such as Amazon’s Alexa and the Google Assistant by the consumers (K.Jones, 2018). In the words of Matt Thompson, chief product officer of Bitty, “voice assistant is the new operating system” used to access data, new information and entertainment by the users. Virtual Personal Assistants (VPAs) are software programmes that allow businesses to interact with customers in a natural way, sorting their questions and responding to their needs. Consumers can give the virtual personal assistant two types of inputs, such as Text and voice interfaces are both available.

References

  1. Almahri, F.A.A.J., Bell, D., & Arzoky, M. (2021). Applications of Machine Learning in Education: Personas Design for Chatbots. In Machine Learning Approaches for Improvising Modern Learning Systems (pp. 72-113). IGI Global.
  2. Bittner, E., Oeste-Reiß, S., & Leimeister, J. M. (2019, January). Where is the bot in our team? Toward a taxonomy of design option combinations for conversational agents in collaborative work. In Proceedings of the 52nd Hawaii international conference on system sciences.
  3. Chaffey, D., & Patron, M. (2012). From web analytics to digital marketing optimization: Increasing the commercial value of digital analytics. Journal of Direct, Data and Digital Marketing Practice, 14(1), 30-45.
  4. Chaves, A.P., & Gerosa, M.A. (2021). How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human–Computer Interaction, 37(8), 729-758.
  5. Cho, C.H., Kang, J., & Cheon, H.J. (2006). Online shopping hesitation. CyberPsychology & Behavior, 9(3), 261-274.
  6. Chung, M., Ko, E., Joung, H., & Kim, S.J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587-595.
  7. de Reuver, M., van Wynsberghe, A., Janssen, M., & van de Poel, I. (2020). Digital platforms and responsible innovation: expanding value sensitive design to overcome ontological uncertainty. Ethics Inf Technol. 22(3), 257-267.
  8. Demetis, D., & Lee, A.S. (2018). When humans using the IT artifact becomes IT using the human artifact. Journal of the Association for Information Systems, 19(10), 5.
  9. Fogg, B.J. (2002). Persuasive technology: using computers to change what we think and do. Ubiquity, 2002(December), 2.
  10. Følstad, A., & Brandtzæg, P. B. (2017). Chatbots and the new world of HCI. interactions, 24(4), 38-42.
  11. Gupta, K., Hajika, R., Pai, Y.S., Duenser, A., Lochner, M., & Billinghurst, M. (2020, March). Measuring human trust in a virtual assistant using physiological sensing in virtual reality. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 756-765). IEEE.
  12. Jain, N., Ahuja, V., & Medury, Y. (2018). Digital marketing optimization. In Digital Marketing and Consumer Engagement: Concepts, Methodologies, Tools, and Applications (pp. 559-567). IGI Global.
  13. Jamil, J.M., Rusle, R., Zolkipli, M.F., & Shaharanee, I.N.M. (2021). Perceived Usefulness of Instagram as a Marketing Tool in Higher Education Institutions. Journal of ICT in Education, 8(1), 104-113.
  14. Janssen, A., Passlick, J., Cardona, D.R., & Breitner, M.H. (2020). Virtual Assistance in Any Context. Business & Information Systems Engineering, 62(3), 211-225.
  15. Jones, V.K. (2018). Voice-activated change: Marketing in the age of artificial intelligence and virtual assistants. Journal of Brand Strategy, 7(3), 233-245.
  16. Kim, J., & McMillan, S.J. (2008). Evaluation of internet advertising research: A bibliometric analysis of citations from key sources. Journal of Advertising, 37(1), 99-112.
  17. Kraus, S., Roig-Tierno, N., & Bouncken, R. B. (2019). Digital innovation and venturing: an introduction into the digitalization of entrepreneurship.
  18. Lehrer, C., Wieneke, A., Vom Brocke, J.A.N., Jung, R., & Seidel, S. (2018). How big data analytics enables service innovation: materiality, affordance, and the individualization of service. Journal of Management Information Systems, 35(2), 424-460.
  19. Liu, B., Xu, Z., Sun, C., Wang, B., Wang, X., Wong, D.F., & Zhang, M. (2017). Content-oriented user modeling for personalized response ranking in chatbots. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(1), 122-133.
  20. Mirsch, T., Lehrer, C., & Jung, R. (2017). Digital nudging: Altering user behavior in digital environments. Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), 634-648.
  21. Mittal, P., & Singh, Y. (2016). Development of intelligent transportation system for improving average moving and waiting time with artificial intelligence. Indian Journal of Science and Technology, 9(3), 1-7.
  22. Nangoy, J.G., & Shabrina, N.H. (2020, November). Analysis of Chatbot-Based Image Classification on Social Commerce LINE@ Platform. In 2020 7th NAFOSTED Conference on Information and Computer Science (NICS) (pp. 232-237). IEEE.
  23. Nguyen, Q.N., & Sidorova, A. (2018). Understanding user interactions with a chatbot: A self-determination theory approach.
  24. Oinas-Kukkonen, H., & Harjumaa, M. (2009). Persuasive systems design: Key issues, process model, and system features. Communications of the Association for Information Systems, 24(1), 28.
  25. Pilato, G., Augello, A., Vassallo, G., & Gaglio, S. (2008). EHeBby: An evocative humorist chat-bot. Mobile Information Systems, 4(3), 165-181.
  26. Schneider, C., Weinmann, M., & Vom Brocke, J. (2018). Digital nudging: guiding online user choices through interface design. Communications of the ACM, 61(7), 67-73.
  27. Todor Raluca, D. (2016). Blending traditional and digital marketing. Bulletin of the Transilvania University of Bratov Series V: Economic Sciences, 9(58).
  28. Todor, R.D. (2016). Blending traditional and digital marketing. Bulletin of the Transilvania University of Brasov. Economic Sciences. Series V, 9(1), 51.
  29. Veklenko, K. (2020). Effectiveness of online advertising: a bibliometric analysis (Master's thesis, University of Twente).
  30. Verma, S. (2021). Bibliometric analysis of research on digital marketing from 2010-20. International Journal of Modern Agriculture, 10(2), 625-640.
  31. Wang, Y.F., & Petrina, S. (2013). Using learning analytics to understand the design of an intelligent language tutor–Chatbot lucy. Editorial Preface, 4(11), 124-131.
  32. Weinmann, M., Schneider, C., & Vom Brocke, J. (2016). Digital nudging. Business & Information Systems Engineering, 58(6), 433-436.
  33. Zhou, L., Gao, J., Li, D., & Shum, H.Y. (2020). The design and implementation of xiaoice, an empathetic social chatbot. Computational Linguistics, 46(1), 53-93.
Get the App