Digital Technology in Marketing Communication.edited

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1 Digital Technology in Marketing Communication Student’s Name Instructor’s Name Course Title Date

2 Introduction The latest wave of the technological revolution has drastically altered the lives of the general populace. It has altered their living, working, and communication styles. The new technology is far greater in scope, size, and sophistication than anything hitherto seen by humans. It is crucial to comprehend how it may affect social and economic life in the future. Its full impact on humanity has yet to be fully understood. This technology gives the company with both opportunities and problems. This technology will boost efficiency and production in the future and usher in an era of market-driven expansion. It will transform supply chains and contribute to the cost-effectiveness of the company. This cutting-edge technology offers the business both opportunities and disadvantages. It is no longer required to wait in line for these activities. It will transform supply chains and contribute to the cost-effectiveness of the company. Nevertheless, it also raises specific concerns over the rise in economic disparity. However, the impact of the new technology on corporate organizations is primarily good. Consequently, it is essential to comprehend how this technology will impact business and communication in the future. The scale of the third revolution is so vast that its socioeconomic effects must be assessed. As individual interactions have changed considerably, enterprises must reconsider their communication strategy. This article discusses the socioeconomic effects of digital technology and its implications for the future generation of marketing communications. The Fourth Industrial Revolution The fourth industrial revolution is mainly centred on data and information rather than technology. Lee et al. (2018) establish that the amount of data gathered from the web and other sources necessitates processing this data to provide relevant results. The quantity of accessible data is so great that the human brain cannot analyze it; thus, it has become essential to create a

3 mechanical brain capable of analyzing all available data (Lee et al., 2018). Consequently, the development of the three most promising ideas of the fourth industrial revolution, namely Big Data, the Internet of Things, and Artificial Intelligence. Big Data The influx of data and information made accessible by the new technology has given rise to the idea of big data. Clark et al. (2020) offer that this applies to various disciplines, from business and finance to environmental and biological studies. The significant data sector is expanding fast and will increase twentyfold during the next decade. Data is an essential aspect of a company. Clark et al. (2020) state that it is projected that manufacturing businesses would keep the most data and customer and operational information that might aid future operations. It is thought that big data will play a significant role in the fourth industrial revolution. Obschonka and Audretsch (2020) support that the cyber-Physical System is intended to make extensive data accessible during the fourth industrial revolution (CPS). The purpose of using big data is to determine whether manufacturing facilities can be made more innovative, allowing for the production of intelligent goods and data transmission as needed. Therefore, it is anticipated to generate flawless items and boost cost-effectiveness without sacrificing product quality. This will allow manufacturers to lower their manufacturing and assembly costs by almost half (Obschonka & Audretsch, 2020). Using big data may potentially lead to significant savings in working capital. The increase in internet searches, social media postings, and communications has enabled Internet corporations to amass vast amounts of information about individuals. In 2012, the World Economic Forum recognized big data as a new form of economic asset. It is anticipated that corporations and governments will use big data (Shaw et al., 2021). However, the use of big data

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4 is not restricted to the commercial sector alone. Numerous disciplines, such as scientific investigation, healthcare, sports, marketing, and academia, have been profoundly impacted by the abundance of data produced in the digital age. It is anticipated that the quantity of data will expedite computer advances, contributing to advances in artificial intelligence via machine-learning algorithms. For example, Apple's Siri is a talking software program. Apple announced it in 2010. Siri responds to the inquiries posed by users based on the Internet-based data accessible (Scott et al., 2022). Siri is growing more competent as a personal assistant software as more and more people want its assistance. The New York police force employs computerized mapping analysis based on historical data on arrest trends, paydays, athletic events, and holidays to identify "hot areas" of crime (Scott et al., 2022). There is a trend towards a more data-driven approach to predicting future outcomes (Shaw et al., 2021). People will depend less on chance and make judgments based on logical analysis of the available data. This is anticipated to alter how people previously lived. The benefits of big data are immense. A recent study by van Riel et al. (2022) indicates that big data may improve customer-centric outcomes by 49%, business optimization by 18%, financial management risk by 18%, new business model success by 14%, and staff cooperation by 4%. Big data enables the collection of client information and analysis of their preferences. This kind of customer analytics aids in predicting the client's future behavior and servicing them accordingly (Van Riel et al., 2022). Such analytics reveal the functionality the consumer likes and the additional functionalities they are willing to embrace. In addition, this would be beneficial for after-sales services since firms could monitor the demands of new clients more proactively and take preventative steps based on data insights.

5 Big data is helping businesses become more clever. Business intelligence and analytics (BI&A) alludes to using substantial data sets and analytical approaches to forecast outcomes (Shaw et al., 2021). It is often used for storing, managing, and visualizing vast amounts of data. Large businesses have used it because it facilitates the storage of rationalized databases supplied by information technology suppliers such as Microsoft, IBM, SAP, and Oracle. Over years of data collecting and storage methods, BI technologies emerged (Shaw et al., 2021). Internet, browser content storage, and mobile technologies introduced e-commerce, e-governance, and scientific and technological advancements (Shaw et al., 2021). These new technologies have ushered in subsequent technological development, analyzing data from multiple sources. The Internet is the primary source for data collecting for most procedures. Multiple IT technologies, including high-speed networks, facilitate this data revolution. The Internet was created in the 1970s, but widespread adoption of the World Wide Web (WWW) did not occur until the 1990s (Onyancha, 2018). Since it has assisted enterprises with data collection and generation, numerous government, e-commerce, and health institutions have embraced Big Data, which has used all the information amassed since the beginning of the Internet (Onyancha, 2018). Thus, Big Data and BI&A contributed to the growth of one of the fastest-growing online and e-commerce industries. Leading Internet businesses such as Amazon, Google, and Facebook are committed to improving cloud computing, online analytics, and social media since these technologies are expected to offer significant future impetus (Onyancha, 2018). Through language and sentiment analysis, social media data is gathered and used to promote products depending on a customer's preferences and mood. Additionally, science and technology employ vast data for study and analysis. Oceanography, astrophysics, and environmental science, among others, have profited

6 considerably from the National Science Foundation's data-sharing initiative (NSF). Many scientific disciplines have used data analytics. One such subject is biology, which has employed NSF data and cyberinfrastructure to create iPlant, a joint initiative to assist students and academics working in plant sciences (Aguado et al., 2019). Similarly, Sloan Digital Sky Survey (SDSS) helps to calculate the position of the stars and make judgments based on this collected historical data (Aguado et al., 2019). In the medical and health business, electronic health records enable practitioners to access a patient's medical record, which promotes a greater comprehension of the patient's medical history and difficulties. Since 9/11, security and public safety have also been a major priority for governments (Aguado et al., 2019). Big data enables the creation of analytics to forecast and prevent violence based on Web-accessible historical and real-time data. Internet of Things The Internet of Things (IoT) has been defined as an "open and complete network of intelligent things" that enables an autonomous organization, information exchange, situational response, and data management (Kumar et al., 2019). It has been regarded as one of the Internet's most potential future breakthroughs. According to Gartner's Hyper Cycle, the market would likely embrace this new technology within five to ten years in 2012 (Kumar et al., 2019). Introducing a massive worldwide network with self-computing and configuration capabilities produces real and virtual "Things" with identities. The topic of how IoT operates emerges. The Internet of Things may be seen as a worldwide computing infrastructure with networked sensory, connectivity, information processing, and communication infrastructure (Kumar et al., 2019). RFID and WSN are core technology of the Internet of Things.

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7 Significant advances in both technologies have led to the growth of the Internet of Things. Kumar et al. (2019) offer that IoT adoption has already begun in the transportation, retail, manufacturing, and pharmaceutical sectors. The development of mobile phone technology, including the growing use of smartphones, wireless communication, and sensor technologies, has significantly influenced information communication and business systems technologies (Kumar et al., 2019). Additionally, several governments have invested in IoT. The government's investment in IoT is anticipated to stimulate industries and transform people's lives. Governments are investing money into IoT to capitalize on the potential for expanding economic development and to foresee future market conditions with the assistance of this new technology, which may not only allow them to improve the market but also prevent financial hazards. In addition, it helps foresee natural disasters and saves cities from such catastrophes. IoT enables companies to remain connected with consumers and provide an unparalleled customer experience (Kumar et al., 2019). Based on the data provided by the Internet of Things, it is feasible to identify, organize, and find novel methods to influence clients and their purchase patterns. Consequently, IoT has the potential to enhance the economic performance of businesses and the economy as a whole. Artificial Intelligence The technology of artificial intelligence (AI) is predicated on the creation of an artificial brain that can enhance and grow via a process known as machine learning. Consequently, the machine will acquire the capacity to enhance its performance and knowledge autonomously. An AI has, for example, perfected the game of Go without human aid (Kar & Kushwaha, 2021). This is a significant advancement for science and technology since it allows for the automation of several operations without the need for repeated command input. AI can revolutionize

8 company operations (Kar & Kushwaha, 2021). Many feel that AI can assist firms in maintaining their competitive edge and launching new enterprises (Kar & Kushwaha, 2021). Numerous businesses implementing AI have been unable to use its possibilities, resulting in gaps. However, many organizations that have effectively implemented AI have significant backing from top management. According to studies on artificial intelligence and corporate adoption, AI is anticipated to significantly influence sectors such as telecommunications, media, and technology. In addition, the primary functional departments in an AI-driven business are anticipated to be marketing, service, information systems, and supply chain automation (Yuan et al., 2019). However, according to MIT Sloan research, fewer than 39% of organizations implementing AI have a plan for using this new technology (Yuan et al., 2019). The highlighted issue is that technological innovation does not offer comprehensive data access. AIs learn via the analysis of data. In most businesses, AI algorithms have unlimited access to organization data, preventing the technology from discovering more about the firm. Companies that have effectively implemented AI have shown deep respect for the need for technology and fostered a culture of system development and learning (Yuan et al., 2019). In several sectors, AI is being utilized to enhance customer service. It is thought that once AI intelligence exceeds human learning and intellect, AI will proliferate and be adopted much more rapidly. Big data, IoT, and Cognitive technologies are the three most recent technologies anticipated to revolutionize corporate operations (Yuan et al., 2019). The general use of these innovations by both commercial and public institutions demonstrates the advent of a new technological era and its pervasive impact on people's lives. Emotional Intelligence Theory and Big Five Trait Theory

9 The Big Five Trait Theory may be utilized in AI-using firms to choose individuals competent to do increasingly complicated service duties. According to Sahin et al. (2019), according to the theory, people may be categorized according to the predominance of five attributes, namely extraversion, social competence, openness, conscientiousness, and neuroticism. Those with the highest degrees of extraversion, openness, openness, social competence, and low rates of neuroticism are likely to be the most effective service employees (Sahin et al., 2019). However, this hypothesis will be more applicable to services requiring superficial emotional participation. Selecting individuals for typically social or emotionally difficult services requires a distinct strategy. Moreover, AI and robots are not yet capable of demonstrating emotional intelligence, a characteristic of influential leaders. Emotional intelligence is the capacity to regulate one's own and other people's emotions by noticing, comprehending, and selecting one's thoughts and feelings (Khasawneh, 2018). Thus, emotional intelligence theory comprises self-awareness, self- management, social intelligence, and social abilities (Khasawneh, 2018). In time, however, robots may imitate the behavioral patterns of emotionally sophisticated leaders. Socio-Economic Impact of Digital Technology While corporations ride the thrill of a new technology revolution, scientists and corporate think tanks are lukewarm about the new advancements. Clark et al. (2020) share that with the Internet, robots, and artificial intelligence, the fourth industrial revolution is expected to increase manufacturing process automation (Lee et al., 2018). From an economic and societal standpoint, this creates several difficulties. Jack Ma, the creator and visionary of the Chinese e-commerce business Alibaba, thinks that as AI becomes cleverer, it will significantly damage people (Jia et al., 2018). Stephen Hawkins, a renowned physicist and Noble Prize recipient, has also raised

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10 doubts over the development and use of artificial intelligence. He notes that the primary problem with AI is the higher intelligence of self-thinking robots (Jia et al., 2018). Hawkins's thesis is that an AI will be more skilled at achieving its objectives owing to its superior intellect, proving that humans are less effective performers. Nonetheless, AIs are being used by organizations across several sectors. With AI, businesses can examine issues and consider all pertinent and contextual facts which align with the problem's description (Clark et al., 2020/. After analyzing this information, the person on the floor will be instantly instructed on how to remedy this issue. Since the AI has all the knowledge of the unit's operation, it also includes information from other parts of the plant, allowing for faster analysis of all the primary data to arrive at a solution (Clark et al., 2020). AI will expedite issue resolution. So, introducing new technology has undeniable benefits and grave drawbacks. New Age Communication and Marketing Marketing and communication are profoundly affected by the advent of digital technology. According to Clark et al. (2020), the new age of business-to-consumer and business- to-business advertising is undergoing significant transformations. Digital marketing formats, such as social media sites like Facebook and Twitter, have rendered conventional media marketing obsolete (Clark et al., 2020). Additionally, B2B marketing is conducted through digital channels. Traditional marketing methods are quickly becoming obsolete. For example, Google has accepted that its present search tool, which was its primary source of income, would soon become outdated and that it must construct a new, mobile-friendly engine capable of meeting the requirements of the new Internet.

11 Big data is unquestionably an essential tool for customer-centric businesses of the future generation. For instance, Facebook gathers data from its users and analyzes the "likes" of demographic cohorts to determine the overall trend of people's attitudes about a particular product, politician, or event (Clark et al., 2020). In addition, cloud computing stores vast quantities of data on the Internet. Amazon, Google, and Facebook are using robot technology to improve productivity. Governments have also begun utilizing robots in order to automate combat. Conclusion The fourth industrial age has the potential to alter human life fundamentally. It has the potential to increase industrial and service industry productivity and efficiency. Innovations such as big data and AI can potentially transform the way that work is performed today. There is both hope and scepticism over the fourth industrial revolution. Concerns abound about the safety and survival of humankind, while hope exists regarding the economic rewards of technological advances. Nevertheless, there is consensus that the fourth industrial age offers several opportunities for the communication and marketing of organizations.

12 References Aguado, D. S., Ahumada, R., Almeida, A., Anderson, S. F., Andrews, B. H., Anguiano, B., ... & Oravetz, A. (2019). The fifteenth data release of the Sloan Digital Sky Surveys: first release of MaNGA-derived quantities, data visualization tools, and stellar library. The Astrophysical Journal Supplement Series , 240 (2), 23. Clark, A., Zhuravleva, N. A., Siekelova, A., & Michalikova, K. F. (2020). Industrial artificial intelligence, business process optimization, and big data-driven decision-making processes in cyber-physical system-based smart factories. Journal of Self-Governance and Management Economics , 8 (2), 28-34. Jia, K., Kenney, M., Mattila, J., & Seppala, T. (2018). The application of artificial intelligence at Chinese digital platform giants: Baidu, Alibaba and Tencent. ETLA reports , (81). Khasawneh, O. Y. (2018). Technophobia without borders: The influence of technophobia and emotional intelligence on technology acceptance and the moderating influence of organizational climate. Computers in Human Behavior , 88 , 210-218. Kar, A. K., & Kushwaha, A. K. (2021). Facilitators and barriers of artificial intelligence adoption in business–insights from opinions using big data analytics. Information Systems Frontiers , pp. 1–24. Lee, M., Yun, J. J., Pyka, A., Won, D., Kodama, F., Schiuma, G., & Zhao, X. (2018). How to respond to the fourth industrial revolution or the second information technology revolution? Dynamic new combinations between technology, market, and society through open innovation. Journal of Open Innovation: Technology, Market, and Complexity , 4 (3), 21.

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13 Obschonka, M., & Audretsch, D. B. (2020). Artificial intelligence and big data in entrepreneurship: a new era has begun. Small Business Economics , 55 (3), 529-539. Onyancha, O. B. (2018). Forty-five years of LIS research evolution, 1971–2015: An informetrics study of the author-supplied keywords. Publishing research quarterly , 34 (3), 456–470. Şahin, F., Karadağ, H., & Tuncer, B. (2019). Big five personality traits, entrepreneurial self- efficacy and entrepreneurial intention: A configurational approach. International Journal of Entrepreneurial Behavior & Research . Scott, A. C., Solórzano, J. R., Moyer, J. D., & Hughes, B. B. (2022). International Journal of Artificial Intelligence and Machine Learning. Shaw, S., Rowland, Z., & Machova, V. (2021). Internet of Things smart devices, sustainable industrial big data, and artificial intelligence-based decision-making algorithms in cyber- physical system-based manufacturing. Economics, Management and Financial Markets , 16 (2), 106–116. van Riel, A. C., Andreassen, T. W., Lervik-Olsen, L., Zhang, L., Mithas, S., & Heinonen, K. (2021). A customer-centric five-actor model for sustainability and service innovation. Journal of Business Research , 136 , 389-401. Yuan, M., Fang, Y., Lv, J., Zheng, S., & Zhou, Z. (2019, June). Research on power trading platforms based on big data and artificial intelligence technology. IOP Conference Series: Materials Science and Engineering (Vol. 486, No. 1, p. 012109). IOP Publishing.

Digital Technology in Marketing Communication.edited

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